The functionality will be available as a software option for the R&S SMBV100B and R&S SMW200A vector signal generators, giving device manufacturers a production-ready pathway to test and validate receiver compatibility with Pulsar signals ahead of the constellation’s commercial deployment.

Pulsar is designed to complement existing GNSS infrastructure — including GPS — by leveraging LEO orbital geometry to deliver stronger signals, improved accuracy, and enhanced resilience against jamming and spoofing. Where legacy GNSS constellations operate in medium Earth orbit at altitudes above 20,000 kilometers, LEO satellites orbit at roughly 500 to 2,000 kilometers, resulting in significantly stronger received signal power and reduced signal travel time. The tradeoff is that individual satellites pass overhead quickly, requiring a larger constellation to maintain continuous coverage — which Xona is building toward commercial scale.

The practical challenge Rohde & Schwarz is addressing is the test gap that precedes a new signal type’s deployment. Before device manufacturers can build and certify receivers that support Pulsar, they need the ability to simulate Pulsar signals in a lab environment — verifying receiver performance against known signal parameters without requiring an operational constellation overhead. Adding that simulation capability to established signal generator hardware provides an accessible, production-scalable route for validation.

“Navigation technology is entering a period of rapid evolution,” said Matt Hammond, North America Satellite Technology Manager at Rohde & Schwarz. “By adding Pulsar signal simulation to our signal generator portfolio, Rohde & Schwarz is preparing our customers for the next evolution of satellite navigation.”

“Test and measurement solutions play an important role in enabling device manufacturers to evaluate compatibility as new signals become available,” said Bryan Chan, co-founder and VP of Strategy at Xona Space Systems. “Rohde & Schwarz brings deep expertise in precision signal generation that helps make this possible.”

The R&S SMBV100B and R&S SMW200A will join Pulsar’s verified ecosystem program, which recognizes devices and test solutions validated for compatibility with Pulsar signals. Rohde & Schwarz showcased its navigation test solutions at Space Symposium 2026 in Colorado Springs this week.

The post Rohde & Schwarz Adds Pulsar Signal Simulation to Vector Signal Generator Portfolio appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

MATTEO PAONNI, ANDREA PICCOLO, LUCA CUCCHI, FRANCESCO MENZIONE, EUROPEAN COMMISSION, JOINT RESEARCH CENTRE (JRC),  OTTAVIO M. PICCHI, EXTERNAL CONSULTANT FOR EC, JRC, STEFAN WALLNER, MARCO ANGHILERI, CÉSAR VAZQUEZ ALOCÉN, PIETRO GIORDANO, EUROPEAN SPACE AGENCY, OLIVIER JULIEN ADVISOR FOR EUROPEAN COMMISSION, DG DEFIS

Exploiting GNSS signals and services has been a major technical development of the last few decades, enabling a large variety of applications to use position, navigation and timing (PNT) information. Starting with GPS and followed by other global and regional GNSS, a multiplicity of signals is now available to worldwide users in the available spectrum allocations concentrated in the L-band. Radio-Frequency Compatibility (RFC)—a concept formally defined many years ago by the International Committee on GNSS (ICG) of the United Nations Office for Outer Space Affairs (UNOOSA)—has been a cornerstone for the coexistence of GNSS signals in any chipset or receiver. These systems can be exploited at user level to provide unprecedented position accuracy and availability, and in some cases even benefit from interoperable signals to achieve superior performance compared to what any single system can do on its own.

Implementing RFC among the GNSS is mostly left to the various system providers, which act upon provisions of the Radio Regulations, established under the International Telecommunication Union (ITU), such as Resolutions 609 and 610 [1,2]. Providers use well-established methodologies to assess compatibility, shaped over many years, that consider the specific characteristics of GNSS systems. These methodologies have been defined considering emissions from Medium Earth Orbit (MEO) and Geostationary Orbit (GEO) satellites, assuming typical power levels on ground within a certain range. 

More recently, novel concepts have been developed to provide PNT signals from satellites in Low Earth Orbit (LEO), generally referred to as LEO PNT. These concepts include exploitation of frequency diversity by transmitting navigation signals in alternative frequency bands such as UHF, the Radio Determination Satellite Systems (RDSS) S-band, or Radio-Navigation Satellite Systems (RNSS) C-band. Most of these new systems also incorporate L-band signals to aid legacy user service adoption. Some LEO PNT initiatives have chosen signals in the L-band at received power levels comparable to classic GNSS, while others are considering significantly higher levels.

This, combined with the fact the world economy now relies heavily on GNSS, makes it crucial to assess the RFC of LEO PNT signals with the billions of terminals that have been designed to process signals transmitted from MEO and GEO satellites. Additionally, it is essential to verify the applicability of well-established methodologies to this new category of signals and systems, with different orbital characteristics and potentially significant variations in power levels at the user terminal input.

This article investigates Radio Frequency (RF) compatibility among LEO PNT and classic GNSS, focusing on the impact at user level and ways to ensure existing and future GNSS receivers will be able to operate reliably in the presence of multiple signals transmitted by LEO PNT systems. To ensure compatibility with existing GNSS, LEO PNT signals shall not cause harmful interference that degrades GNSS receiver performance, which is typically measured by the effective C/N₀ level. We pay specific attention to RFC in L-band RNSS allocations, which is already densely populated by many regional and global PNT systems.

Compatibility as a Key Prerequisite for Interoperability 

In the last 20 years, with the proliferation of global and regional GNSS, compatibility has been the central pillar to ensure these systems can be developed and operated without mutually interfering. GNSS providers have prioritized compatibility to ensure their coexistence and in 2007 agreed on a unified framework to define compatibility under the ICG, recognizing compatibility as a critical enabler of interoperability. By harmonizing technical standards and operational practices, the ICG laid the groundwork for global users to leverage multiple GNSS systems without compromising performance or reliability.

At its core, compatibility refers to the ability of space-based PNT services to function independently or in combination without causing mutual interference. This principle ensures signals from different GNSS systems can coexist in the RF spectrum while maintaining their individual characteristics and performance. For users, compatibility translates into robust, uninterrupted access to GNSS services, which is vital for applications ranging from aviation and maritime navigation to precision agriculture and autonomous vehicles. Achieving this requires meticulous coordination among GNSS providers to mitigate risks of signal degradation or service disruption.

A key driver behind this focus is the scarcity of RF spectrum allocations for GNSS. As the number of providers and their demand for spectrum grows, the RF bands reserved for satellite navigation face increasing congestion. This is particularly true for the so-called E1/L1 band (1559-1610 MHz) and E5 band (1164-1215 MHz) [3]. GNSS providers, therefore, must adopt a cautious approach to compatibility to safeguard these critical spectrum resources. This involves rigorous analysis of signal characteristics, orbital parameters and interference thresholds to prevent mutual degradation. 

Assessing RFC is a multidimensional task that requires balancing technical, operational and regulatory considerations. The primary objective of RFC analysis is to protect GNSS users by ensuring signals can be processed effectively. This involves evaluating potential interference scenarios, modeling signal interactions, and defining mitigation strategies. Over the years, methodologies for RFC assessment have evolved to incorporate modeling tools and standardized assumptions, including a dedicated methodology established under ITU-R M.1831[4]. These include:

• Orbital and signal parameters: modeling of satellite orbits, signal structures (e.g., modulation schemes, bandwidths) and transmission frequencies

• Payload and antenna characteristics: modelling of relevant satellite payloads characteristics, including antenna radiation patterns and power levels

• User receiver models.

Two critical metrics are central to RFC computations: Spectral Separation Coefficients (SSC) and Aggregate Gain (G_agg). The SSC is the primary tool to assess the potential risk of interference between two signals due to their capability to share a frequency band efficiently.

The G_agg represents the equivalent gain to be considered when a certain power is transmitted by a satellite constellation with certain transmitting antenna characteristics and specific orbital parameters and received by a receiver with a representative antenna pattern.

Over the last 20 years, RFC assessments have focused on GNSS systems transmitting signals from MEO, which is commonly adopted by all global systems, and from GEO and IGSOs/HEOs, used by regional systems and satellite-based augmentation systems (SBAS). It is essential to ensure methodologies, standard assumptions and typical use cases are adequate once new systems are transmitting from LEO.

LEO PNT Key Differentiators 

LEO PNT systems represent a transformative approach to satellite navigation, offering distinct advantages such as providing enhanced signal strength at ground level, which can be obtained more efficiently than from MEO altitudes thanks to the reduced distance to the final users and hence smaller free-space propagation losses. These constellations can also offer improved coverage in urban and high-latitude regions, therefore complementing traditional MEO constellations. However, these systems also introduce unique compatibility challenges that necessitate rigorous evaluation to ensure harmonious coexistence with existing GNSS. 

LEO satellites operate at significantly lower altitudes than the MEO altitudes of traditional GNSS. This proximity to Earth enables LEO PNT systems to potentially ensure higher ground signal strength, which enhances signal robustness. However, this advantage comes with a critical caveat: High power levels from LEO satellites can interfere with legacy GNSS receivers, which are designed to operate with extremely weak signals from MEO satellites (typically in the range of –150 dBW to 160 dBW in the case of open, unobstructed reception). 

The dynamic nature of LEO orbits introduces additional complexities for receiver design. LEO satellites move rapidly relative to Earth, resulting in significant Doppler shifts and short visibility periods. From a compatibility perspective, based on available information regarding forthcoming LEO PNT systems [9], the number of simultaneously visible satellites is expected to be higher than that of legacy GNSS. This results in an increased number of transmitters operating within the same spectrum bands.

Two scenarios warrant particular attention: 

1. Impact on legacy GNSS users: The coexistence of high-power LEO signals with weak MEO signals in shared bands (e.g., E1/L1) could degrade legacy GNSS receiver performance. For instance, a LEO transmitter operating at +10 or even +20 dB in the E1 band could overwhelm a GNSS signal at –160 dBW, even with substantial spectral separation. 

2. Compatibility for space users: LEO PNT systems also must avoid interfering with other space-based receivers in LEO, which often rely on GNSS signals for autonomous navigation and other critical functions. 

The impact from high power might be (partially) mitigated through spectral separation and other specific measures, but the risk remains high, so all factors must be carefully assessed. Spectral separation represents a key design tool to minimize interference among two signals transmitted within the same band, and the choice of the carrier frequency remains highly critical. However, the spectrum currently allocated to RNSS is very crowded, and the possibility to have completely isolated signals is extremely limited, especially when transmitted at high power. In this condition, spectral separation is never corresponding to complete isolation and, as such, especially in the presence of very high power levels, the impact might still be relevant. 

Spectral Separation

The RNSS E1/L1 band is increasingly congested with the presence of multiple signals from various global satellite constellations. Current band allocation is the result of decades of bilateral coordination, which has ensured highly compatible systems with good spectral separation among signals. It is aimed at enhancing interoperability among the systems, which often use the same carrier and spreading modulation. However, it is essential to note good spectral separation does not guarantee perfect signal isolation.

The Figure 1 left plot illustrates this concept, showing the spectral separation coefficients between existing GNSS signals transmitted by GPS, Galileo and BeiDou and a hypothetical additional BPSK(1) signal placed at various frequencies within the E1/L1 band. This plot presents the spectral separation coefficient for the BPSK(1) signal, while the right plot shows the same concept for a BPSK(2) signal. Both plots show how challenging it is to identify slots that ensure very high or high spectral separation with all existing signals in E1/L1, given the presence of a wide variety of signals transmitted in the band. In particular, the central part of the band is populated by several signals adopted for open services by most of the global and regional systems, while governmental signals from GPS, Galileo and BeiDou occupy higher frequency slots. Any offset between -20 and 20 MHz from the E1 carrier frequency results in an SSC above -80 dB/Hz with a given signal already transmitted in the band. It is important to note that even an SSC of -80 dB/Hz, which may seem low, corresponds to a certain degree of “non isolation,” which might become especially crucial in the case of a high power (or high aggregate gain) from the interfering signal/system. Leveraging spectrally efficient modulations, like what Xona plans to use, [5] can certainly help to improve the isolation. 

Beyond the potential impact related with compatibility, the actual added value of high power for final users is to be well understood, as system self-interference is also to be accounted for. The combined (positive) effect of increased power needs to be adequately assessed against the increased self-interference to avoid a saturation of the effective signal to noise ratio when the amount of satellites in view increases.

Consideration of High-Power Systems 

RF compatibility between a signal of interest delivered by constellation X and an interfering signal delivered by constellation Y relies on assessing the amount of noise created by inter- and intra-system interference. This noise is typically represented as an additional white noise at the correlator output that has a level equal to

Where 

is the maximum received power of the interfering signals, assuming all constellation satellites transmit at that power;

is the Spectral Separation Coefficient (SSC) between the interfering signal and the local signal used by the receiver to process the useful signal;

is the so-called aggregate gain and represents a coefficient that accounts for the aggregation of the power of all the interfering signals (including the effect of the user antenna) affecting the user receiver.

At the end, the total equivalent noise that will affect the reception of the useful signal can be modeled as a White noise with the following level:

Where

is the thermal noise affecting the receiver;

are the number of signal types broadcasted by the constellation delivering the useful signal of interest and the equivalent noise generated by these signal types, respectively;

are the number of signals broadcasted by other constellations and the equivalent noise generated by these systems, respectively.

Self-Interference 

Among the current GNSS signals used, let us look at GPS L1 C/A:

• It is one of the signals that leads to the highest SSC (-61.9 dB/Hz) regarding self-interference due to the frequency compactness of BPSK(1). 

• Accounting for a typical G_agg for a global system of 11 dB in an open sky situation 

• The typical maximum power for a GNSS system is in the order of -153 dBW.

Taking these values into account, GPS L1 C/A generates an additional equivalent self-interference White noise of about -203.9 dBW/Hz, which is slightly lower or equal to typical thermal noise (roughly between -200 and -204 dBW/Hz). Assuming the receiver thermal noise is at a level of -201.5 dBW/Hz, this means the GPS L1 C/A self-interference would increase the background noise (or equivalently, reduce the C/N₀) by about 2 dB if it was the only source of interference.

If the maximum received power of GPS L1 C/A was much higher, then self-interference would start dominating the thermal contribution. This would eventually result in background noise increasing at the same rate as the power of the useful signal. Figure 2 shows the expected C/N₀ (not accounting for the gain brought by the user antenna on the useful signal) as a function of the maximum power of the GNSS signal for a variety of modulation. The C/N₀ reaches a ceiling at some point due to self-interference. This ceiling depends on the modulation because each modulation will create a distinct SSC.

Another effect occurs during constellation build up. In this case, the amount of self-interference will also grow, which can be represented as a growth of the G_agg. This is illustrated in Figure 3 for a BPSK(1) signal. As the Gagg increases, the C/N₀ decreases. To better understand this figure, a G_agg of 3, 6 and 9 dB can be seen as equivalent to receiving one, two and four signals, respectively, at the indicated received power. For a high-power constellation, this decrease can be relatively steep, depending on the type of signal used. 

Figures 2 and 3 highlight that high power signals might not lead to the expected high C/N₀ for a typical MEO or LEO constellation in open sky situations. This has implications regarding the quality of the measurements, which would not be as improved compared to a “normal” system. Still, there are advantages to such high-power signals:

• If the receiver is not in an open sky situation, fewer satellites will be in view. This is equivalent to reducing the self-interference through a lower G_agg. So, in this case, the C/N₀ at receiver level would become higher, as shown in Figure 3.

• There is still a better resistance against interference compared to “normal” signals because this interference would need to be more powerful to have an effect on the C/N₀.

Finally, in a complicated environment where some signals will be received with a high C/N₀ while others are severally attenuated, it is possible the power difference between both types of signals becomes significant. Imagine a signal at 70 dB-Hz and another at 25 dB-Hz. Even though the power difference is 45 dB, both could be tracked. However, unless the isolation of the spreading codes is extremely good, the cross-correlation due to the signal with a high C/N₀ will be higher than the auto-correlation of the weak signal, thus making it difficult or unreliable to acquire/track the weak signal.

Sharing the Band

The previous section only considered self-interference, as if there was only a single constellation. However, many GNSS systems share the L-band. This brings diversity to users and requires compatibility. This means we must consider that users will not only suffer from self-interference, but also interference from other systems. If we consider these are only global systems with a G_aggof about 11 dB, then the level of additional noise created by these interferences will depend on the number of interfering systems, the power of the interfering signals and the SSC between signals.

Take the example of the compatibility between GPS L1 C/A and Galileo BOC(1,1) signals. Figure 4 represents the expected C/N₀ (not considering the effect of the receiving antenna on the useful signal) for a L1 C/A receiver and for a BOC receiver for a plurality of received powers for both signals (all signals of the constellation are assumed to have the same received power). Increasing the power of one of the two signals is always detrimental to the other; the area where both signals have a good C/N₀ is somewhere around the diagonal. Thus, it makes sense for the power of both signals to be roughly the same. So, if there’s a high-power signal in part of the band, other signals broadcasting there have to use high power signals, unless they are isolated spectrally. 

In reality, more than two signals or systems are typically considered in similar frequency bands, thus facilitating interoperability. Imagine there are three systems (for instance GPS/Galileo/BeiDou) all broadcasting BOC signals (data and pilot) for interoperability reasons, as is the case currently at 1575.42 MHz. Assuming all signals have roughly the same received power and G_agg, Figure 5 shows using high power signals would result in a higher loss on the C/N₀. This leads to a situation in which the C/N₀ is not so different between high power signals and “normal” signals (3dB difference in C/N₀ for a difference in the received power of 30 dB). This shows the current situation is well adapted and optimized for compatibility and interoperability. Note the band around 1575.42 MHz is even more crowded as it contains signals from GPS, Galileo, BeiDou, QZSS, NavIC, SBAS, etc. In such a situation, it is questionable whether it makes sense to have high power signals.

The Case of LEO GNSS Space Users 

In traditional GNSS operated from MEO satellites, space-based users experience similar signal reception characteristics to ground users in terms of received power, G_agg, and C/N₀ degradation. This assumption no longer holds when GNSS is operated from LEO satellites, which are slightly above the altitude of space-based users. To evaluate the power variation, Table 1 analyzes individual contributions for a reference ground user and three GNSS space users at different altitudes, corresponding to three EU Copernicus Sentinel satellites currently flying at about 600, 700 and 800 km, respectively [6]. The single satellite power increase was computed by accounting for the reduced path loss, while the G_agg was computed assuming the receivers move on a sphere with a radius equal to the sum of the Earth’s radius and the victim satellite’s altitude. As the space user approaches the interfering constellation orbit altitude, the number of simultaneous interfering satellites decreases, leading to a reduced G_agg. By combining the single satellite power increase with the reduced G_agg, we can determine an approximate increase in interference power from the alternate system a space user would experience with respect to a ground user. Table 1 shows that, for Sentinel 3A, the interference increase can be up to nearly 10 dB. This increase might have significant implications for GNSS receiver performance. This kind of impact should be carefully considered in the design and operation of LEO-based GNSS systems.

Interference Impact on Space Receivers

The risk of interference from an alternate system on GNSS signal reception can be significant when the system transmits from LEO orbits instead of MEO.

An increasing number of space missions use GNSS space receivers for constellation management and to provide their services. Earth observation satellites like the Sentinels of EU Copernicus system use Precise Orbit Determination (POD) [7] based on GNSS observables for georeferencing their images; communication satellites like Starlink and OneWeb might take advantage of GNSS reception for timing and pointing their inter-satellite links as well as for beam pointing. For scientific missions like GRACE-FO, Swarm and ICESAT-2, GNSS POD is required to perform the measurements. 

A higher noise floor caused by receiving high-power navigation signals from LEO would cause GNSS space receiver accuracy to degrade, with a direct impact on these missions.

The undesired effects are a degradation in the quality of data provided as well as issues in data distribution among satellites caused by poor inter-satellite network synchronization. Also, constellation management and collision avoidance manoeuvres rely on the accuracy of GNSS-based measurements and would suffer in harmful interference scenarios.

On top of this, there is another category identified as “super-users” who exploit GNSS for operations and service provisions. Notably, LEO PNT providers operate in the low Space Service Volume (SSV) and LEO region [8] at an altitude that exceeds several LEO missions. LEO PNT systems with a constellation altitude lower than one of the alternate systems might be heavily impacted on GNSS signal reception. LEO PNT relies on GNSS signal measurements for several key navigation payload functions. Unlike other GNSS, the Orbit Determination and Time Synchronisation function is not based on the observables collected by a ground network of receivers, but is largely based on space receiver measurements which, depending on the particular architecture, are processed on-board the satellite or downlinked to the ground segment to compute the LEO PNT satellite’s accurate orbit and clock.

This information is used by the on-board timing subsystem to estimate the offset of the onboard clock with respect to GNSS time (and possibly steering the clocks to the desired timescale) as well as to estimate the satellite orbit and generate the navigation message parameters to be broadcast to LEO PNT users to compute their PVT solution.

In [9], Earth Observation Copernicus Sentinels are considered critical GNSS users in space. In the paper, a visibility analysis assesses the potential impact “super-users” might be subject to and focuses on ensuring uninterrupted operation of these critical space-based assets. It also demonstrates how the potential risk is not related to short or temporary “collisions,” but rather the continuous exposition to a potentially very high amount of interference from a relatively short distance.

Therefore, degraded GNSS space receiver accuracy caused by interference can significantly impact the quality of the generated LEO PNT signals as their frequencies, PRN codes, navigation message timestamps and orbit and clock corrections are all based on it. 

Degradation of GNSS space receiver accuracy would result in a higher value of the Signal-In-Space-Error contribution and a degradation of the PVT solution computed by users.

An Example of Interference in Space

Through the Copernicus program, The European Union (EU) is operating a fleet of Earth Observation satellites, referred to as Sentinel satellites. The satellites embark GNSS space receivers that provide Galileo and GPS code and phase iono-free measurements for POD and Time Synchronization (TS).

The detailed assessment in [10] showcases how ground based interference is measurably impacting space receivers on LEO satellites, although the GNSS antenna is mounted on the relevant satellites facing in zenith direction. The interference originates from the ground and affects a short portion of the satellite’s trajectory. In a future scenario, a comparable level of interference may originate from satellites emitting RNSS signals at a slightly higher altitude compared to the Earth observation satellite. In such a case, the direction of the interference would align with the pointing of the Earth Observation’s GNSS antenna; no favourable low gain of the antenna would reduce the level of interference. 

Figure 6 shows the C/N₀ average as measured by the Sentinel 2C on-board GNSS receiver for the Galileo E1-C signal component as a function of the satellite’s ground track in February 2025.

A clear reduction in C/N₀ over eastern Europe can be identified. It ranges up to a level of approximately 4 dB. This reduced C/N₀ can be attributed to the jamming events occurring over the corresponding region. 

The impact of the interference event on February 1, 2025, on the real-time PODTS (in blue for the broadcast products and in orange for the Galileo High Accuracy Service (HAS) [11] products) is shown in Figure 7. The red vertical lines indicate the start of the interference event. The plots show the kinematic TS and POD performance respectively. The error increases in both cases until the number of satellites is not sufficient to compute position or time. The kinematic method cannot cope with measurement gaps (in this case only four satellites were available). The impact of the interference on both POD and TS is clearly evident.

Conclusions 

The use of high-power signals transmitted from LEO satellites can provide interesting performance benefits when considered in isolation. However, when multiple systems transmit signals in the same frequency slot, it can lead to interference and performance degradation. To avoid this, most systems would need to use high-power signals, which would ultimately lead to a decrease in overall performance and hardly sustainable spectrum consumption.

A careful approach to compatibility has been a fundamental factor to ensure protection of the very scarce and increasingly crowded spectrum allocations available to GNSS providers. 

• GNSS providers adopted key principles for open/commercial signals in L-band

• Sharing the band without exclusive use of a spectrum portion 

• Interoperability at user segment level is enabled by compatibility at system level.

The risk of a “power race” among commercial providers in legacy GNSS bands (E1/L1 and E5/L5) could disrupt these practices and penalize legacy users as well as future providers willing to access the spectrum. Among various risks, a power escalation could prevent other providers from using this part of the band without significant degradation.

The exploitation of GNSS by space users in LEO poses a significant risk, particularly for space service users like the Copernicus Sentinels. These aspects must be carefully assessed, and the case of space users should be studied in detail when evaluating compatibility between MEO GNSS and LEO PNT systems.

It is essential that all new LEO PNT providers ensure a sustainable approach to spectrum. Multilateral fora, such as the International Committee on GNSS (ICG), and GNSS providers can help build guidelines to ensure long-term sustainable spectrum access for the benefit of users and potential future providers.

Regulatory elements, such as ITU Resolution 609 and Recommendation 608, exist to help keep the level of interference low and prevent one system from dominating the available margin to the emission limit. These instruments are essential for ensuring spectral sustainability and should be respected by all operators. 

Acknowledgements 

This article is based on material presented in a technical paper at ION GNSS+ 2025, available at ion.org/publications/order-publications.cfm.

References 

(1) Resolution 609 (Rev.WRC-07) Protection of aeronautical radionavigation service systems from the equivalent power flux-density produced by radionavigation-satellite service networks and systems in the 1 164-1 215 MHz frequency band, RES609-1 (2007). https://www.itu.int/en/ITU-R/space/Res609%20CM%20Documents/RES-609_e.pdf

(2) Resolution 610 (REV.WRC-19) Coordination and bilateral resolution of technical compatibility issues for radionavigation-satellite service networks and systems in the frequency bands 1 164-1 300 MHz, 1 559-1 610 MHz and 5 010-5 030 MHz, RES610-1 (2019). https://www.itu.int/en/ITU-R/space/Res609%20CM%20Documents/RESOLUTION%20610%20(Rev%20WRC-19).pdf

(3) European Union. (2023). European Union, Galileo Open Service Signal-In-Space Interface Control Document (OS SIS ICD). https://www.gsc-europa.eu/sites/default/files/sites/all/files/Galileo_OS_SIS_ICD_v2.1.pdf

(4) Recommendation ITU-R M.1831-1 (09/2015) A coordination methodology for RNSS inter-system interference estimation, M.1831-1 (09/2015) (2015). https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.1831-1-201509-I!!PDF-E.pdf

(5) Reid, T. G. R., Neish, A. M., Walter, T., & Enge, P. K. (2018). Broadband LEO Constellations for Navigation. NAVIGATION, 65(2), 205–220. https://doi.org/10.1002/navi.234

(6) Sentinel Online. (2025). Copernicus Programme. https://sentinels.copernicus.eu/web/sentinel/copernicus

(7) European Union. (2025). Copernicus Operations—POD in details. https://sentiwiki.copernicus.eu/web/precise-orbit-determination

(8) FrontierS. (2024). State of the Market Report, Low Earth Orbit Positioning Navigation and Timing–2024 Edition. frontiersi.com.au

(9) Paonni, M., Picchi, O. M., Piccolo, A., Cucchi, L., Menzione, F., Wallner, S., Anghileri, M., Alocén, C. V., Giordano, P., & Julien, O. (2025). On the Compatibility of GNSS User Segment with Emerging LEO-PNT Systems and Signals. Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), 915–928. https://doi.org/10.33012/2025.20406

(10) De Oliveira Salguiero, F., Lapin, I., Cordero Limon, M., Caparra, G., & Garcia Molina, J. A. (2025, September). Impact of Ground-Based Interference on GNSS Space Receivers On-Board LEO Satellites. Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024).

(11) European Union, ‘European GNSS (Galileo) High Accuracy Service Signal-In-Space Interface Control Document (HAS SIS ICD)’. (2022, May). https://www.gsc-europa.eu/sites/default/files/sites/all/files/Galileo_HAS_SIS_ICD_v1.0.pdf.

Authors

Matteo Paonni is Deputy Head of the Space, Connectivity and Economic Security Unit at the Joint Research Centre of the European Commission in Ispra, Italy. He coordinates JRC technical and policy support to the EU Satellite Navigation Programmes within the European Commission. Matteo is also the chairman of the Galileo 2nd Generation Signals Task Force (G2G-STF), established under the EU Space Programme. Before joining JRC in 2013, he was a research associate at the Institute of Space Technology and Space Applications at the University of the Federal Armed Forces in Munich.

Ottavio M. Picchi holds an MSc in Telecommunications Engineering and a Ph.D. in Information engineering, both from the University of Pisa. Since 2012, he has worked in signal processing for communications and navigation. He is an external consultant for the European Commission’s Joint Research Centre, focusing on Fused PNT systems, 5G NTN, IRIS2 and RF compatibility.

Andrea Piccolo is a Technical and Scientific Officer at the European Commission’s Joint Research Centre in Ispra, Italy. His specialties include GNSS spaceborne receivers, space service volume, LEO PNT, Galileo PRS, and developing new Galileo services. He graduated with an M.Sc. in Telecommunications Engineering from Politecnico di Milano in 2014. He worked as a Radio Navigation System Engineer at Thales Alenia Space Italy, focusing on GNSS Spaceborne receivers and Galileo Navigation Signal Generation Unit (NSGU) product development from 2015 to 2023.

Luca Cucchi is a GNSS Security and Galileo PRS Security Officer at the Joint Research Centre (JRC) of the European Commission in Ispra, Italy. He coordinates the activities of the JRC Galileo PRS User Segment Laboratory and provides support to the Directorate-General for Defence Industry and Space (DG DEFIS) and EU Agency for the Space Programme (EUSPA) on Galileo Program activities. He has 14 years of experience in the private sector as a radio navigation engineer, primarily focusing on the Software Defined Radio approach. He earned his Master’s Degree in Telecommunication Engineering from the University of Pisa in 2005.

Francesco Menzione received a master’s degree (2012) and Ph.D. (2017) from the University of Naples Federico II in Aerospace Engineering and Satellite Navigation. From 2012 till 2021, he worked in the aerospace sector as a navigation and control engineer. In 2021, he joined European Commission’s Joint Research Centre as Technical and Scientific Officer. In this role, he provides technical and project management support for various DEFIS-funded studies and research areas, with a focus on Precise On-Board Orbit Determination using HAS, Space Service Volume, LEO-PNT, Hybrid PNT, 5G-NTN, LEO-based RFI monitoring, and GNSS-based remote sensing.

Stefan Wallner is the Head of the Galileo Signal-in-Space Engineering Unit within the Navigation Directorate at the European Space Agency. He graduated with a Diploma in Mathematics from the Technical University of Munich and was research associate at the University of the Federal Armed Forces in Munich. He has worked at the European Space Agency since 2010 and is responsible for the Galileo Signal in Space and Performance Engineering activities within ESA.

Marco Anghileri is the satellite Payload Manager of Celeste, ESA’s program for satellite navigation in Low Earth Orbit. He has more than 20 years of experience in satellite navigation across academia, industry and the European Space Agency. He began his career in 2005 at the Universität der Bundeswehr München, contributing to Galileo signal innovations later adopted in both first- and second-generation systems. He served as Lead Systems Engineer and Project Manager at IFEN GmbH and Airbus Defence and Space, where he led international R&D activities on future GNSS signals and system architectures for ESA and the European Commission. From 2021 to 2025, he was part of ESA’s GNSS Evolution team, conducting LEO-PNT system studies and technology R&D activities, while also taking responsibility for frequency management and security in the evolution of Galileo and EGNOS.

César Vázquez Alocén holds a M.Sc in industrial engineering from the University of Alcala (Spain). Since 2018 he has worked at ESTEC (ESA) in different roles, mainly working on GNSS signal design, signal processing algorithms and receiver testing activities, supporting several ESA navigation programs including Galileo and LEO PNT.

Pietro Giordano covers the role of LEO PNT system manager at the European Space Agency. Previously, he worked in Thales Alenia Space Italy before joining ESA/ESTEC in 2009. He worked several years within the Galileo project covering many roles, from user segment to operations, and in the ESA technical directorate as overall coordinator for spaceborne GNSS and space PNT technologies. He has been in charge of the definition and coordination of the European technology harmonisation roadmap for on-board radio navigation receivers and he supported Earth observation programs (e.g.: Copernicus/Sentinel). He contributed in the development of new concepts such as real-time on-board autonomous POD (P2OD concept), LEO PNT payloads, definition of new spaceborne GNSS receiver components (e.g.: AGGA family ASIC) and use of GNSS signals for lunar autonomous navigation. He was the chain lead for the navigation services within the ESA Moonlight program.

Olivier Julien is an advisor to the European Commission DG DEFIS on EU Satellite Navigation Programs where he supports the EU new initiatives on navigation and radio-frequency matters. From 2019 to early 2025, he was a Senior Principal Engineer in the Positioning technology team of u-blox (Switzerland). Before that, he was the head of the Signal Processing and Navigation research group of the TELECOM laboratory of ENAC (France). He received his engineering degree from ENAC and his Ph.D. from the University of Calgary (Canada).

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Pulsar operates from low Earth orbit as a commercial alternative to GPS, designed to address the jamming, spoofing, and signal-strength vulnerabilities that have increasingly exposed legacy navigation infrastructure’s limitations in both military and civilian contexts.

The facility produces satellites whose signals are up to 100 times stronger than traditional GPS and accurate to two centimeters, operating in low Earth orbit 20 times closer to Earth than existing GPS infrastructure. Pulsar is designed to work with existing GPS devices — a design choice enabled by Xona’s decision to move from C-band to L-band frequencies after determining that most users lack compatible C-band equipment. “Compatibility with existing user equipment was critical to scaling,” said Brian Manning, Xona’s co-founder and CEO. 

The factory opening follows a $170 million Series C closed in late March, led by Mohari Ventures Natural Capital with participation from Craft Ventures, ICONIQ, Woven Capital, NGP Capital, Samsung Next, Hexagon, and other investors. “This factory is how we move from proof-of-concept to active global infrastructure,” Manning said. “We’ve already demonstrated how the technology works, now it’s about manufacturing and deploying our constellation faster than anyone thought possible.”

At full production, the company aims to manufacture more navigation satellites per week than the U.S. currently produces in a year, with a target of deploying the full 258-satellite constellation for the cost of a single GPS satellite on orbit today. 

The defense dimension was central to the opening remarks. “Anything that moves, anything that needs to know where it is, is a potential customer of ours — including the Department of Defense,” Manning said. “We’re not built as a defense contractor necessarily, but we are proud of the work that we do with the U.S. government and other governments.” The Space Force has already awarded Xona a Strategic Funding Increase (STRATFI) agreement combining $20 million in government funding with $30 million in private capital, as military interest in alternative PNT capabilities grows amid increasing reliance on GPS in contested environments. 

Manning also described Xona’s singular position in the regulatory landscape. The company is the first commercial operator approved by the FCC to broadcast on the GPS frequency spectrum alongside sovereign navigation systems. “We were sitting in rooms with China, Russia, Europe and Xona,” Manning told the San Francisco Business Journal. “It was an area that no commercial company has ever gone into.”

The broader commercial picture is one of infrastructure inadequacy meeting an autonomous-systems moment. “This new era of technology is largely here — cars driving themselves, robots, mobile devices, physical AI, wearables, autonomous farm tractors,” Manning said at the opening. “All of these things share one fundamental thing in common: to operate safely, to operate safely at scale, they simply need to know where they are.” “It’s ignoring the underlying challenge that the infrastructure was not built to do what everyone is trying to use it to do today,” he added. “That’s what we’re building — an entirely new infrastructure.” 

Rep. Kevin Mullin (D-CA) spoke at the ceremony, framing the facility in terms of national competitiveness. “The question to the United States is simple — will we lead this era of navigation, or will we follow?” Mullin said. “We’ve seen navigation disrupted in critical shipping lanes, driving gas prices up for everyone.”

Over a dozen commercial receiver partners are already tracking signals from Xona’s first production-class satellite, launched in June 2025. Six additional satellites are planned for a SpaceX rideshare mission in Q4, with broader deployment expected in 2027. Trimble — an investor and customer whose VP spoke at the ceremony — announced a collaboration with Xona in 2025 to integrate its correction services with Pulsar, targeting centimeter-precision positioning across construction, agriculture, and geospatial markets.

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JIHYE PARK, JACLYN J. BOHN, OREGON STATE UNIVERSITY

While the primary purpose of the Global Navigation Satellite System (GNSS) was positioning, navigation and timing (PNT), it has been widely used for environmental monitoring for several decades. As GNSS signals propagate from space to Earth, they interact with various layers of the atmosphere, carrying vital information about the terrestrial environment. In the upper atmosphere, the distribution of electron content in the ionosphere can be measured to observe space weather events [1, 2]or geophysical activities, such as earthquakes and volcanic eruptions that trigger traveling ionospheric disturbances [3-7]. Similarly, signal delays caused by the troposphere provide essential data for monitoring meteorological events [8,9].

Whereas atmospheric effects occur along the direct signal path, environmental sensing at the Earth’s surface is achieved by analyzing “multipath” signals. Traditionally, multipath is considered a critical positioning error to be mitigated. However, these reflected signals contain specific physical information about the reflecting surface itself.

In 1993, Martin-Neira first introduced the concept of using GNSS multipath for environmental sensing—a field now known as GNSS-Reflectometry (GNSS-R). Subsequent research demonstrated the capability of GNSS-R for altimetry; Martin-Neira successfully measured water surface height by computing relative delays between the direct signal and its reflected counterpart [10]. Further geodetic applications were explored by [11], which analyzed the geometry of reflected signals using dual-polarized (right-handed circular polarized, or RHCP, and left-handed circular polarized, or LHCP) antennas.

A more recent and practical evolution of this technology is GNSS-Interferometric Reflectometry (GNSS-IR), introduced by research such as [12] and [13]. Unlike traditional GNSS-R, which requires specialized LHCP antennas to capture reflections, GNSS-IR uses standard RHCP antennas. This technique analyzes the interference pattern created when direct and reflected signals overlap at the antenna. While these interference patterns are present in code pseudorange and carrier phase observations, they are most effectively analyzed through the Signal-to-Noise Ratio (SNR) [12]. 

By modeling the SNR as a function of the satellite elevation angle, researchers can extract the multipath components generated by a planar reflector. Because it uses existing geodetic infrastructure and standard hardware, GNSS-IR offers a powerful and cost-effective method for long-term environmental monitoring, particularly in the study of land-fast ice.

Surface Sensing via SNR 

The primary data source for GNSS-IR is the SNR. In a typical GNSS receiver, the SNR is influenced by signal strength and the antenna gain pattern, which generally increases as a function of the satellite elevation angle. However, the presence of multipath reflections introduces a distinct oscillatory pattern into the SNR data.

By analyzing the frequency of these multipath-driven oscillations, the vertical distance (h) between the antenna’s phase center and the reflecting surface can be determined. According to [12], the frequency of the oscillation (f) remains constant when mapped against the sine of the satellite elevation angle. This relationship is defined by the geometric distance between the antenna and the reflector and the wavelength of the signal (λ): f=2h/λ. Consequently, if the oscillation frequency of the SNR and the specific GNSS wavelength are known, the height of the reflecting surface—such as sea level or ice thickness—can be precisely computed.

Figure 1 illustrates the overall workflow for estimating the vertical distance between an antenna and the reflecting surface by extracting multipath effects from the triple frequency GPS SNR signals, S1, S2 and S5. 

In Figure 1a, the amplitude of SNR increases with the increasing satellite elevation angle, and the presence of oscillation pattern is also seen. To isolate the multipath effect, the trend is removed as shown in Figure 1b, denoted as detrended SNR (dSNR). The representing frequency of this oscillation can be found through a spectral analysis shown in Figure 1c. Each detrended SNR spectra have distinctive dominant frequencies with notable high-power spectrum, which presumably came from a planar reflector causing the multipath. The dominant frequency of each signal is converted to the vertical distance between the antenna and the reflector, that is the height of the surface, h, by taking into account the wavelength of each signal, λ. Consequently, the dominant peaks of three signals are aligned with similar heights as shown in Figure 1d.

While the theoretical framework of GNSS-IR suggests all frequencies should yield identical height measurements for a single reflector, practical observations often reveal slight misalignments, as seen in Figure 1d. This phenomenon arises because different signals—such as GPS L1 (1.575 GHz) and L2 (1.227 GHz)—interact with the reflecting surface differently. Factors like the antenna gain pattern and the surface’s electrical properties create a frequency-dependent scale error [14].

To ensure consistency, many researchers traditionally rely on a single frequency, often favoring GPS L2 because of its higher sensitivity to multipath effects [12]. While this approach provides stable returns, it disregards the redundant information available from modern triple-frequency GNSS signals.

Our research [14, 15] takes a different path by leveraging the full spectrum of available observations (e.g., GPS L1, L2 and L5). We have found that while undetectable biases between frequencies do exist, their magnitude is generally smaller than the inherent observational noise of the GNSS-IR method as [14] reported the scale errors of L1 and L2 as 13 mm and 15 mm, respectively. By analyzing all three signals simultaneously, we can implement a “majority vote” logic to select the most reliable dominant peak. For example, Figure 2 illustrates the spectral power of detrended SNR (dSNR) for GPS L1, L2 and L5. In this instance, L2 and L5 show clear dominant peaks at a height of approximately 7.4 m, while L1 initially shows its strongest spectral power at 7.1 m. By examining the local maxima of the spectrum rather than just the single highest peak, we can identify a secondary peak that aligns with the 7.4 m measurement confirmed by the other two frequencies. This multi-frequency cross-verification significantly reduces the risk of outliers and improves the overall resilience of the monitoring system, especially in complex environments like the frozen Arctic.

A practical consideration in GNSS-IR is that retrieving the reflector height at a specific epoch requires a sufficient duration of time-series observations to characterize the oscillation pattern. This presents a challenge when monitoring dynamic surfaces, such as tidal water levels or moving ice, where the height is constantly changing. To capture this motion while maintaining high-precision results, we apply a sliding window to the dSNR data. This technique limits the observational period of the input signal to a specific temporal “snapshot” that accurately represents the surface height at that moment without sacrificing the frequency of the output.

In [16], we identified an optimal configuration for this sliding window by carefully balancing the sliding interval and the window width. While the interval can be adjusted based on the desired temporal resolution of the final data, the window width requires more precise calibration. It must be sufficiently large to capture the multipath-driven oscillations necessary for spectral analysis, yet narrow enough to prevent the “smearing” of dynamic surface behavior. Ultimately, the proper width is determined by the expected vertical distance between the antenna and the reflecting surface, ensuring the spectral peaks remain sharp even as the environment shifts. More exhaustive details on these parameter selections are provided in [15]. 

Monitoring Land-fast Ice in Alaska

GNSS-IR based tidal monitoring is particularly advantageous in extreme environments such as the Arctic and Antarctica. In these regions, accurate water level observations are vital, yet conventional tide gauges face significant operational hurdles. Because these instruments require direct contact with the water, their installation and maintenance are frequently compromised by harsh conditions and the periodic formation of ice. These limitations were addressed by using data from existing GNSS stations in Alaska to monitor tidal motion [15]. Our study identified and navigated specific high-latitude challenges, including reduced satellite visibility compared to mid-latitude regions and signal quality degradation caused by ionospheric scintillation. Despite these atmospheric and geometric constraints, we confirmed GNSS-IR serves as a robust and valid tide gauge alternative in the Arctic.

Because GNSS-IR measures the characteristics of the reflecting surface, the technique is applicable to monitoring both open water and ice surfaces. In Alaska, land-fast ice forms along the coastlines every autumn and persists until it melts away in the spring. Monitoring this nearshore ice is of critical importance for coastal communities, marine wildlife and regional navigation. Establishing seamless, year-round observations is therefore essential for coastal hazard preparedness and the safety of marine transportation.

To address this need, our research group at Oregon State University (OSU) developed the GNSS-R Water-Ice observation system (GRWIS). The primary objectives of the GRWIS are threefold: to monitor tidal motion regardless of the presence of sea ice, to detect the onset of ice formation, and to assess the dynamic motions of land-fast ice. To validate the system’s performance, a GRWIS station was established in Nome, Alaska (64° 29′ 44.5″ N, 165° 26′ 20.0″ W), operating alongside a traditional tide gauge to provide a direct comparison of the GRWIS solutions.

The GNSS installation in Nome consists of a Septentrio PolaRx5S receiver and a single, upward-facing choke ring antenna mounted on a pier directly overlooking the ocean. This setup is strategically positioned opposite to an operational tide gauge, which is part of the National Oceanic and Atmospheric Administration (NOAA) National Water Level Observation Network (NWLON, Site ID: 9468756). Unlike the GNSS-IR system, which senses the surface remotely, this tide gauge is a contact-based sensor that is heated and structurally protected from the extreme open-ocean conditions of the Arctic. This specialized protection ensures a continuous record of water level estimations throughout the year, providing a reliable and high-fidelity benchmark against which the GRWIS solutions can be validated across both open-water and ice-covered periods.

Water Level Estimation Using GRWIS in Nome

The experimental results from the Nome station demonstrate that GNSS-IR remains highly effective for water level estimation even under the rigorous constraints of high-latitude environments. While data acquisition and processing in these regions are often complicated by limited satellite geometry, ionospheric scintillation and extreme weather, our analysis showed strong agreement with the NWLON benchmark. Figure 4 illustrates a comparison between the GRWIS-derived water levels and the NOAA tide gauge during an ice-free period from November 2 to 8, 2023.

In the upper plot of Figure 4, the GNSS-IR based estimations (depicted in blue) closely track the ground-truth water levels provided by the NWLON tide gauge (depicted in grey). The lower plot quantifies the precision of the system, presenting the discrepancy between the two datasets. The residuals vary within a narrow range of approximately -5 cm to +5 cm, confirming the GRWIS system can achieve geodetic-grade accuracy for tidal monitoring despite the technical challenges inherent to the Arctic.

Sea Ice Detection

Monitoring sea ice using ground-based GNSS-IR is an emerging area of research, building on established principles of signal coherence and surface roughness. Early investigations by [17] in Disko Bay, Greenland, demonstrated a clear relationship between the number of coherent reflected signal observations and the presence of sea ice. This supported the earlier theoretical framework by [18], which posited that increased surface roughness on the open ocean significantly influences GNSS-R signal coherence. To quantify this, [19] introduced a damping coefficient derived from the attenuation of dSNR time series. While this coefficient includes unmodeled elevation-dependent effects, it serves as a proxy for reflector height variance and the physical state of the surface. More recently, [20] used GNSS interference frequencies to monitor sea ice in Finland. They noted that during frozen periods, the discrepancy between GNSS-IR height and mean sea level corresponds to the “total freeboard” (ice plus snow accumulation), which can be converted into ice thickness via hydrostatic balance equations.

Our research group at OSU has advanced these methods by introducing a numerical indicator called the Confidence Level of Retrieval (CLR). The CLR is the ratio between the amplitude of the dominant spectral peak and the average amplitude of the remaining peaks in the SNR spectrum. It provides an intuitive measure of how clearly a single reflecting surface—such as calm water or flat ice—stands out against background noise or surface scattering.

As illustrated in Figure 5, the spectral domain changes significantly based on surface conditions. In calm conditions (a), the dominant peak is sharp and unmistakable, resulting in a high CLR. Under rough conditions (b), the spectral power is distributed across multiple peaks (marked with “X”), lowering the relative strength of the dominant peak. We have found the CLR performs comparably to the damping coefficient used by [21] for monitoring wave heights, suggesting the CLR is a robust tool for characterizing the transition from turbulent open water to stable ice.

A year-long analysis of CLR data from Nome (November 1, 2023, to October 31, 2024) reveals a distinct seasonal signature that aligns with the formation and retreat of land-fast ice. During the ice-free period from November to early December, the moving average CLR remains relatively low at approximately 17 ± 9. Occasional dips below a value of 5 in this period correspond to rough sea states and high wave action. However, as land-fast ice stabilizes in mid-December, the CLR shifts dramatically, increasing to an average of 38 ±7. This high-confidence state persists until June 2024, when the spring melt commences and the CLR returns to its baseline ice-free average of 14 ± 9. This clear threshold behavior confirms the CLR is a highly effective metric for the automated detection of sea ice presence.

Spectral Analysis Using Continuous Wavelet Transform

To further analyze the complex frequency components of the GNSS-IR data, we employed the Continuous Wavelet Transform (CWT). Unlike the standard Fourier Transform or the Lomb-Scargle Periodogram, which decompose a signal into infinite sine waves and lose temporal localization, the CWT uses shifted and scaled versions of an original “wavelet”—an asymmetric, wave-like oscillation that begins and ends at zero [22]. This allows for the simultaneous extraction of instantaneous frequencies and their corresponding amplitudes over time, providing a clear advantage for monitoring non-stationary signals like those found in dynamic Arctic environments.

Our study specifically focused on data from April 2024, a period characterized by the simultaneous presence of sea ice and snow cover. Using estimation techniques, we compared GNSS-IR height measurements to the NOAA tide gauge benchmark after removing outliers exceeding three standard deviations (Figure 7a). A persistent offset was observed between the two datasets, largely attributable to snow accumulation on the land-fast ice. Because GNSS-IR signals reflect off the uppermost surface—in this case, the snow—the measurement represents the combined height of the water, ice and snow. In contrast, the heated, sheltered tide gauge measures the water level exclusively. By subtracting the tide gauge data from the GNSS-IR observations (Figure 7b), we effectively isolated the “deviation” signal, removing the dominant tidal motion to focus on higher-frequency surface dynamics. 

The CWT output is visualized as a magnitude scalogram, where the x-axis represents time, the y-axis represents frequency, and the color intensity indicates the strength of the correlation between the signal and the wavelet. Within these scalograms, the Cone of Influence (COI)—indicated by dashed lines—marks the boundary where edge effects from the wavelet transform become significant. Data within the COI provides an accurate time-frequency representation, while results outside it are potentially influenced by the finite length of the time series.

Analyzing the scalograms in Figure 8 reveals distinct differences. The tide gauge scalogram shows almost no high-magnitude regions at higher frequencies, consistent with a sensor protected from surface noise, while the GNSS-IR scalogram displays numerous high-magnitude “bright spots” at high frequencies. While some of this is inherent noise from multiple reflection points within the Fresnel zone, these signatures also capture non-tidal surface movements. By removing the low-frequency semidiurnal tidal components shown in Figure 8c, the remaining high-frequency signatures are isolated. While some outliers remain, these scalograms confirm GNSS-IR is capturing surface reflections beyond simple tidal oscillations.

Through visual inspection of the scalograms, we identified a dominant energy band between approximately 2.0×10^-5 Hz and 2.6×10^-5 Hz. Using a bandpass filter, this region of the plot is isolated between 1.8×10^-5 Hz and 2.8×10^-5 Hz to calculate the period. The dominant period for the GNSS-IR data is 12.06 hours, and for the tide gauge data it is 12.83 hours, both of which align closely with the expected ~12-hour cycle of semidiurnal tides. 

To isolate this tidal motion from the GNSS-IR data, we low-pass filtered at 2.6×10^-5 Hz. After filtering, the majority of high-magnitude values at high frequencies are removed (Figure 9a). The low-pass filtered GNSS-IR water levels follow the tidal motion (Figure 9b). As shown in Figure 9, this filtering effectively removed the high-frequency magnitude peaks, resulting in a smoothed GNSS-IR water level time series that closely follows the tidal benchmark while maintaining the seasonal surface offset.

Detecting Snow Accumulation

Once land-fast ice is established, the reflecting surface effectively becomes a multi-layer system consisting of water, ice and accumulating snow. Because the GNSS-IR antenna captures reflections from the uppermost interface, its height measurements incorporate the total thickness of the snow and ice layers. In contrast, the heated and sheltered NOAA tide gauge continues to measure the water level exclusively. This disparity provides a unique opportunity to isolate snow depth by comparing the two datasets.

Figure 10a displays the low-pass filtered GNSS-IR water levels alongside the tide gauge data during the fieldwork period of April 3 to 7, 2024. At the beginning of this interval, the discrepancy between the sensors is near zero; however, the offset increases and fluctuates as the fieldwork progresses. We evaluated this difference by applying a six hour moving average to suppress uncharacterized high-frequency oscillations, revealing a clear trend in the surface elevation.

To validate whether this offset truly represents snow accumulation, we compared the GNSS-derived difference against in situ data from three snow surveys conducted during the fieldwork (Figure 10b). The GNSS-IR results summarized in Table 1 show reasonable agreement with the average snow depth and standard deviation from these physical surveys, supporting the hypothesis that the measurement offset is a direct proxy for snow depth.

Summary and Future Work

This study demonstrates that GNSS-IR is a versatile and robust tool for monitoring the Arctic’s complex, multi-layered environments. By using the GRWIS system in Nome, Alaska, we successfully established a method for independently estimating tidal motion, snow accumulation, and the dynamic signatures of land-fast ice from a single geodetic-grade receiver.

Our application of CWT proved instrumental in identifying the distinct frequency components within the reflected signals. By establishing precise cutoff frequencies, we were able to effectively low-pass filter the GNSS-IR data, isolating the semidiurnal tidal cycles from high-frequency surface noise. A key finding of this work is the characterization of the vertical offset between the remote GNSS-IR measurements and contact-based tide gauge data. By correlating these offsets with in-situ measurements, we have shown this discrepancy serves as a reliable proxy for snow depth, effectively turning a measurement “error” into a valuable environmental metric.

While the results are promising, the current validation of the snow-depth estimation is constrained by the relatively short duration of the April 2024 field campaign. A limited observational window for ground-truth data inherently restricts our ability to characterize the system’s performance across the full spectrum of Arctic weather events, such as heavy storm surges or rapid mid-winter melt-refreeze cycles.

Moving forward, we intend to expand our research to more precisely identify the specific spectral frequencies that correspond to snow depth and ice deformation. Future work will involve longer-term validation campaigns and the development of automated algorithms to separate snow accumulation from ice-loading events. By refining these multi-frequency GNSS-IR techniques, we aim to provide coastal Arctic communities with a maintenance-free, year-round solution for monitoring the increasingly unpredictable dynamics of land-fast ice. 

Acknowledgment

The authors gratefully acknowledge the National Science Foundation (NSF) for its support of this research through the Arctic Observational Network (AON) EAGER grant #2321313. We also extend our gratitude to the local community in Nome, Alaska, and the NOAA Integrated Ocean Observing System (IOOS) for providing the critical benchmark data used in this study.

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(19) Strandberg, J., Hobiger, T., & Haas, R. (2019). Real-time sea-level monitoring using Kalman filtering of GNSS-R data. GPS Solutions, 23, 61. https://doi.org/10.1007/s10291-019-0851-1

(20) Regmi, A., Leinonen, M. E., Pärssinen, A., & Berg, M. (2022). Monitoring sea ice thickness using GNSS-interferometric reflectometry. IEEE Geoscience and Remote Sensing Letters, 19, 2001405. https://doi.org/10.1109/LGRS.2022.3198189

(21) Roggenbuck, O., Reinking, J., & Lambertus, T. (2019). Determination of significant wave heights using damping coefficients of attenuated GNSS SNR data from static and kinematic observations. Remote Sensing, 11(4), 409. https://doi.org/10.3390/rs11040409

(22) Wang, X., He, X., & Zhang, Q. (2019). Coherent superposition of multi-GNSS wavelet analysis periodogram for sea-level retrieval in GNSS multipath reflectometry. Advances in Space Research, 65(7), 1781–1788. https://doi.org/10.1016/j.asr.2019.12.023

(23) Azeez, A., Park, J., & Mahoney, A. (2025). Preliminary results of nearshore ice and water level monitoring in Arctic using single antenna ground-based reflectometry. In Proceedings of the 2025 International Technical Meeting of The Institute of Navigation (ION ITM 2025) (pp. 216–228). https://doi.org/10.33012/2025.19982

(24) Bohn, J.J., J. Park, A. Mahoney, E. Fedders (2026), Monitoring the Dynamic Motion of Landfast Ice in Alaska Using GNSS-Interferometric Reflectometry (GNSS-IR), Proceedings of the 2025 International Technical Meeting of The Institute of Navigation, Anaheim, California, January, 2026.

Authors

Dr. Jihye Park is an associate professor of Geomatics in the School of Civil and Construction Engineering at Oregon State University (OSU). Before joining OSU, she worked as a post-doctoral researcher in Nottingham Geospatial Institute at University of Nottingham, UK. She holds a PhD in Geodetic science and surveying at The Ohio State University. Her research interests include GNSS positioning and navigation, Precise Point Positioning, Network Real-time kinematic, GNSS meteorology, GNSS-Reflectometry, and GNSS remote sensing for monitoring the earth environments, natural hazards, as well as artificial events.

Jaclyn Bohn is a graduate student at Oregon State University in the School of Civil and Construction Engineering studying geomatics. She received her bachelor’s degree in mathematics from the University of Utah. Her research interests lie in applications of GNSS-Reflectometry to monitor coastal areas.

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ESA launched the first two Celeste in-orbit demonstration satellites — IOD-1 and IOD-2 — on March 28, marking the agency’s first step toward extending satellite navigation into low Earth orbit. u-blox, working under ESA’s Navigation Innovation and Support Program (NAVISP) Element 2, is conducting a technical evaluation of how LEO signals interact with and augment established GNSS constellations such as Galileo.

The Swiss positioning firm frames LEO not as a replacement for GNSS but as an additional layer — one characterized by higher signal strength and rapidly changing satellite geometry that could accelerate convergence and improve robustness in challenging signal environments. Early integration work is underway on u-blox’s X20 GNSS platform, examining how LEO signals across multiple frequency bands can be incorporated into future receivers.

The scope of the NAVISP project includes characterization of emerging LEO signal transmissions, analysis of LEO-GNSS measurement interactions, and evaluation of how dynamic satellite geometry affects positioning performance.

“Our work within the ESA NAVISP framework allows us to better understand how emerging signal sources can complement GNSS and contribute to robust and reliable positioning performance,” said Jani Käppi, Head of Technology Positioning at u-blox.

The full Celeste demonstration constellation will ultimately comprise 11 satellites testing innovative signals across various frequency bands. ESA’s 2025 Ministerial Council further endorsed a next phase — an LEO-PNT In-Orbit Preparatory phase — and incorporated Celeste as one of three pillars of its new European Resilience from Space initiative. 

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Issued through Army Contracting Command – Aberdeen Proving Ground via a C5 prototyping project, the awards carry a combined estimated value of up to $41 million over a 36-month period of performance. Both vendors will develop MOSA-compliant, modular, and upgradable solutions emphasizing non-radio frequency technologies to address GPS-denied and -degraded environments.

PM PNT’s Modernization product office launched the NorthStar effort in August 2023 with a virtual industry day and request for information that drew 27 vendor responses. Those responses, along with technical evaluations and white paper reviews, shaped the program’s tiered capability structure and drove the decision to split the award between multiple vendors.

“Awarding to multiple vendors encourages competition, speeds up implementation and integration of new technology to meet emerging threats, and reduces cost of engineering change proposals,” said Erik Scott, product manager for PNT Modernization.

Contract kickoffs with both vendors are scheduled for next month, with design reviews and a soldier touchpoint to follow.

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Unlike many PNT-related events, the Defense Strategies Institute’s annual Assured PNT (APNT) Summit has always been focused on businesses interested in government direction and policy. Developments in technology are discussed of course, but attendees also hear from government leaders about everything from high-level strategy to, in some cases, the right staff person to contact about contracting opportunities. 

I was privileged to again be the moderator at the most recent summit, which was held, as always, in the Washington, D.C., area. This year, I was struck by the general consensus among attendees on a number of issues spanning technology and policy. 

The event is governed by the Chatham House Rule. So, while I can relate what was said, who said it, their organizations, etcetera must remain, with a few exceptions, confidential. In fairness, though, all of the following points were mentioned by more than one speaker and there seemed to be general agreement among the 200+ attendees.

The theme of the summit was “Developing a Robust Resilient National PNT Architecture for U.S. Dominance.”  

Here are some of my takeaways:

Requirements

While all users and applications have specific PNT requirements, many folks don’t really know what they need and just default to GPS/GNSS. When asked about performance requirements for future PNT systems, many program managers respond by asking about what is possible. This “what do you need?”/ “what can I have?” cycle can be frustrating for providers.

Adoption 

Adoption often requires integration and can be expensive. User equipment (MGUE) for M-Code was given as an example several times. Putting satellites in space is less expensive than the required networks and user equipment on the ground. Modular open system architectures (MOSA) will help with new builds, but legacy systems are a significant challenge.

Integration

Integration is difficult and expensive. Platforms are highly varied and it is expensive to develop and execute a program to integrate new signals and systems for each one. The sheer number of platforms that use GPS is huge. Again, MOSA will help a lot, but its biggest impact will be with new systems.

Dual Civil/Military 

America’s experience with implementation and adoption of GPS can provide a number of lessons for future systems. Among them is the benefit of dual use—military and civilian. That’s fostered through a virtuous cycle of broad research, lower size-weight-power-cost user equipment economic order quantities, and greater adoption.

PACE

Primary, Alternate, Contingency, Emergency (PACE) was cited several times as a systems engineering way to look at a layered PNT approach. GPS/GNSS will be primary for the foreseeable future. Emergency will likely always be paper maps, looking out the window, dead reckoning, and the like. This helps organizations focus on determining which systems should be used for Alternate and Contingency.

Quantum

Practical and affordable clocks and sensors are getting closer. Size, weight, power and cost still remain challenges. Engineering work to enable manufacture at scale is also needed. The technology has the potential to be widely commercialized in 10 years or so.

“America is so far behind!”

Most attendees found a presentation by Wing Commander Mark Brammer from the United Kingdom both inspiring and a bit discouraging (he was happy to have his remarks exempted from the Chatham House Rule). Brammer discussed the need to move away from over-dependence on space and how the UK established a cross-government office to coordinate national PNT efforts to ensure both military and civil PNT needs are met. 

He described Britain’s plan for a resilient national PNT architecture, which has been funded and is being executed. It involves a very robust fiber timing network with three timing centers, an eLoran network that will serve the British Isles and adjacent maritime approaches, and integration with space-based PNT sources. 

Governance

Numerous comments from panelists and attendees identified governance as the primary reason the U.S. is so far behind the U.K. in the journey to PNT resilience. 

Multiple mature technologies are available. Cost was not seen as a principal obstacle, and the phrase “for less than the cost of putting one MEO satellite in space …” was heard more than once. 

The group consensus was that America’s governance structure is so dispersed, we are handicapped in our ability to decide and act. One attendee commented “Everyone is responsible, so no one is responsible.”

This view has also been articulated by the President’s National Space-based PNT Advisory Board, though in that case U.S. PNT resilience was compared to that of China. APNT governance shortfall was also called out by attendees at September’s PNT Leadership Summit hosted by the RNT Foundation and Inside GNSS.

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The signal was received at 10:38 CET on April 8, 2026, and verified by ESA teams at ESTEC as well as at GMV’s monitoring station in Lisbon.

The milestone marks successful commissioning of the spacecraft and opens the operational experimentation phase of a program designed to test whether a complementary low Earth orbit navigation layer can enhance Galileo’s accuracy, resilience, and security. Celeste IOD-1 and a second demonstrator, IOD-2, were launched March 28 aboard a Rocket Lab vehicle from Launch Complex 1 in Mahia, New Zealand. The two satellites — built by separate European consortia, with GMV leading one and Thales Alenia Space the other — separated from the launch vehicle approximately one hour after liftoff. LEOP and commissioning activities for IOD-1 were conducted by an integrated GMV and Alén Space team from the mission control center in Tres Cantos.

Operating at altitudes between 500 and 560 km, the demonstrators will validate precise autonomous orbit determination without ground infrastructure dependence and will test navigation signal performance in L- and S-bands from LEO. The program’s rationale is multi-orbit resilience: by integrating a LEO constellation alongside Galileo’s medium Earth orbit architecture, Celeste aims to reduce vulnerability to interference and expand the envelope of advanced PNT services available to European users.

The IOD phase will comprise eleven satellites plus one in-orbit spare across both consortia. GMV holds prime contractor responsibility for six of the demonstrator satellites, covering system definition and design, space and ground segments, user segment, and operations. The two initial demonstrators are focused on securing registered frequency allocations and signal testing through the end of 2025. Eight larger follow-on satellites are under development, with subsequent launches targeting 2027 and the eventual fielding of a full operational fleet.

GMV was selected by ESA in 2024 to lead one of the two parallel Celeste development contracts.

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GABRIEL THYS SAFRAN ELECTRONICS & DEFENSE, AND FÉDÉRATION ENAC ISAE-SUPAERO ONERA, UNIVERSITÉ DE TOULOUSE; CHRISTOPHE MACABIAU, JULIEN LESOUPLE, JÉRÉMY VÉZINET, ANAÏS MARTINEAU FÉDÉRATION ENAC ISAE-SUPAERO ONERA, UNIVERSITÉ DE TOULOUSE; RAPHAEL JARRAUD

The approach phase is one of the most safety-critical segments of a civil aircraft flight. Within the framework of Performance-Based Navigation (PBN), navigation systems must satisfy strict requirements in terms of accuracy, availability, continuity and integrity [1]. These constraints become particularly stringent during the final segment of a precision approach, which extends from the Final Approach Point, approximately 7 nautical miles from the runway threshold, down to the decision altitude [2].

Aircraft guidance during this phase traditionally relies on the Instrument Landing System (ILS) or on Global Navigation Satellite Systems (GNSS) augmented by space-based (SBAS) or ground-based (GBAS) augmentation systems [2]. However, conventional radionavigation infrastructures are progressively being reduced to a Minimum Operational Network intended to mitigate large-scale GNSS outages [3]. As a result, modern precision approaches increasingly depend on augmented GNSS solutions. In practice, the radio-frequency environment around airports may be affected by Radio Frequency Interference (RFI), which can degrade or interrupt GNSS signals. Such disruptions may force aircraft to interrupt the approach and revert to the remaining conventional navigation aids. Ensuring operational continuity, therefore, requires complementary sensors that are passive and robust to RF disturbances.

Optical sensors constitute promising candidates, particularly during the approach phase when the aircraft operates close to the ground and the visual environment provides rich navigation data. Although commercial aircraft are already equipped with onboard cameras to enhance pilot situational awareness during approach, landing and taxiing, these sensors rarely provide operational credit, and their potential remains largely underexploited.

Vision-based navigation relative to the runway has attracted increasing research interest. The European Japanese VISION project developed a hybrid inertial-GNSS-vision navigation system based on an error-state Kalman filter accounting for image processing delays [4]. The C2Land project, led by the Institute of Flight Guidance at Technische Universität Braunschweig, investigates autonomous landing at airports without ground infrastructure by fusing optical and inertial data with non-augmented GNSS [5]. Flight experiments conducted within this project represent some of the most advanced demonstrations of vision-based navigation systems.

Despite these developments, integrating cameras into safety-critical navigation architectures raises important integrity challenges. Vision sensors introduce new failure modes that must be incorporated into the integrity monitoring framework with appropriate risk allocation. However, integrity monitoring methods for vision-based navigation remain relatively limited. Many approaches adapt algorithms originally designed for GNSS, such as RAIM-based techniques using synthetic measurements derived from visual landmarks or batch implementations [6,7] or extensions of AIME using multiple optical sensors [8]. More recent work proposed protection level formulations for hybrid inertial-vision-GNSS systems considering multiple fault modes [9].

However, as highlighted in the survey by [10], the direct application of GNSS integrity methods to vision measurements is generally suboptimal due to the specific characteristics of optical observations and the limited availability of statistical models describing their integrity behavior. This lack of operational experience complicates compliance with the stringent integrity requirements of civil aviation precision approaches as it requires conservative assumptions.

This study builds upon the hybrid inertial-vision-GNSS system introduced by [11], which is designed to ultimately comply with the performance requirements of a PBN CAT I precision approach. It aims to characterize the impact of vision integration on continuity and integrity requirements and to derive false alarm and missed detection probabilities that an integrity monitoring algorithm must verify.

Navigation Dual Navigation System Design

Navigation System Assumptions

The navigation system considered in this article is designed to support PBN CAT I precision approach operations. The system is set in the context of a radio frequency environment potentially disturbed by jamming or spoofing, resulting in potential GNSS service loss of continuity or unavailability. In the PBN framework, any such GNSS event during a precision approach would trigger a navigation system alert, requiring the pilot to initiate a missed approach procedure [1]. 

The hybrid navigation system integrates measurements from four distinct sensors, including

• A navigation-grade inertial measurement unit (IMU) providing high-quality angular and velocity increments

• A GNSS receiver processing satellite signals (Signal-In-Space) and SBAS corrections to compute a 3D position

• A barometric altimeter used to stabilize the IMU’s vertical channel, supplying altitude information

• A vision system composed of one or more imaging sensors (e.g., monocular, stereo, infrared) and an image processing unit. 

The selected vision-based navigation approach relies on landmark-based positioning [12]. The optical sensors observe the aircraft’s environment, referred to as the scene, and specifically detect the runway, from which one or more landmarks are extracted. The 3D positions of these landmarks are supposed to be a priori known and retrieved from the Aeronautical Information Publication (AIP). By associating each landmark with its known coordinates, a line-of-sight vector between the camera and the landmark can be reconstructed. This line-of-sight serves as the vision measurement input to the estimation process. A tightly coupled integration scheme is hence considered in this architecture. The data fusion and state estimation process is based on an error-state Kalman filter [13]. The filter’s structure, along with the mathematical modeling of its propagation and measurement equations, are detailed by [11].

The hybrid navigation system provides guidance system estimates of key navigation parameters, including position, velocity and attitude. It is also designed to provide integrity monitoring and issue alerts in the event of a continuity loss. In parallel, a predefined flight path is derived from a waypoint database and provided to aircraft guidance. This guidance is ultimately used by the flight crew.

Single-Filter Architecture Limitations

A straightforward extension of an SBAS-augmented inertial-GNSS navigation system consists of integrating vision measurements within a triple inertial-GNSS-vision hybrid architecture. Such integration can significantly improve continuity of service because vision measurements can compensate for temporary GNSS outages. In this configuration, a loss of continuity would only occur if both GNSS and vision measurements become unavailable simultaneously. This capability is particularly valuable given the increasing vulnerability of GNSS to RFI.

However, the introduction of vision also brings additional failure modes that must be considered in the integrity risk allocation. When these failure modes are incorporated into the integrity framework, they may inadvertently tighten the integrity requirements associated with the SBAS-augmented GNSS subsystem. Consequently, improving continuity through sensor redundancy does not automatically translate into improved system integrity and may even degrade it if failure dependencies are not properly managed. The limitations of such triple-hybrid architectures are discussed in [14]. 

The integration of vision into an inertial-GNSS hybrid navigation system introduces a fundamental technical challenge arising from two partially conflicting objectives: 

• To increase the continuity of service by leveraging vision measurements to bridge potential GNSS service losses

• To ensure this integration does not increase the integrity requirements allocated to the SBAS-augmented GNSS system.

Technical Solution: Dual Navigation Architecture

To resolve this trade-off, this work proposes a dual-navigation architecture in which vision measurements are integrated without increasing the integrity constraints imposed on the GNSS subsystem. The core principle of this architecture lies in the implementation of two parallel navigation solutions. 

The first, referred to as the Main Navigation, relies solely on measurements from the GNSS, the navigation-grade IMU and the barometric altimeter, deliberately excluding any vision data. As such, this navigation chain corresponds to a state-of-the-art SBAS-augmented inertial-GNSS navigation system. 

In contrast, the second solution, referred to as the Vision Navigation, uses only the IMU, barometric altimeter and vision-based measurements, excluding any GNSS inputs. It thus forms a pure inertial-vision navigation system. 

During a precision approach conducted by a civil aircraft, the navigation outputs, comprising the estimated navigation states (position, velocity and attitude) as well as the associated integrity monitoring functions and alerts, are provided by either the Main Navigation or the Vision Navigation subsystem. By default, the system delivers navigation outputs from the Main Navigation as long as the SBAS-augmented GNSS service is available. When the GNSS service becomes unavailable and is formally declared out of service, the navigation outputs are transferred to those generated by the Vision Navigation. This transition is handled by a dedicated switching mechanism whose operation is governed by the availability status of the augmented GNSS service. 

The use of two parallel navigation solutions, therefore, enable a clear separation of integrity risks associated with GNSS and vision within their respective navigation chains. The resulting dual-navigation configuration is illustrated in Figure 1. 

The proposed architecture benefits from the well-established performance of the inertial-GNSS hybrid system as long as GNSS signals are available, thereby maintaining the integrity of a navigation solution that has already been extensively validated. At the same time, it ensures continuity of service in the event of a GNSS outage by incorporating vision-based measurements into the overall navigation process. From the user’s perspective, the system continues to provide the required navigation information without indicating whether it originates from the main or vision-based navigation branch. 

Starting Point of the Study

Assumptions and Definitions 

Integrity and continuity allocations for the hybrid navigation system are analyzed using fault/risk allocation trees that describe the logical relationships between failure modes and their causes. The interpretation and computation rules of these trees are defined in [2]. 

Integrity represents the level of trust in the correctness of the navigation information and includes the system’s ability to provide timely alerts [2]. An integrity failure occurs when the Navigation System Error (NSE) exceeds the horizontal or vertical alert limits, producing a Hazardous Misleading Information (HMI) event. This event can be expressed as [17]:

where e denotes navigation error, AL the operational alert limit, y the vector of measurements and Ω the set of measurements considered consistent with the integrity monitor. The integrity risk corresponds to the probability that this event occurs without triggering an alert within the specified time-to-alert [2].

Continuity refers to the system’s ability to perform its function without interruption, assuming it is available at the beginning of the operation. Although a precision approach typically lasts about 150 seconds, the continuity risk defined in [2] only concerns the final 15 seconds of the approach. Continuity loss events include integrity monitor alerts, unscheduled GNSS outages, and RFI disturbances. From a fault detection perspective, these events are primarily driven by detection alarms, which are generally dominated by false alarms. GNSS outages occurring earlier in the approach are instead classified as losses of availability.

Risk Allocation for a SBAS Augmented Inertial-GNSS Navigation System

To derive the fault allocation tree for the proposed hybrid navigation system, a reference allocation model is first established based on an SBAS-augmented inertial-GNSS architecture. The resulting structure, illustrated in Figure 2, follows the fault allocation framework developed for SBAS-based APV and CAT I approaches by [15].

The top-level metric is the Target Level of Safety (TLS), defined as the acceptable hull-loss probability per aircraft per flight hour. For approach operations, the TLS is 1×10^-8 per approach, assuming a standardized duration of 150 seconds. Considering one catastrophic accident is associated with approximately 10 incidents, the associated risk budget becomes 1×10^-7, which is equally allocated to continuity and integrity branches.

To derive system-level requirements, an additional breakdown is required. This refinement incorporates the mitigating influence of the flight crew. Operational analyses indicate reduction factors of seven for integrity and 2,000 for continuity, reflecting the fact continuity losses occurring during the final seconds of an approach can often be managed visually, whereas integrity failures may generate misleading guidance.

After applying these factors, the navigation system requirements for PBN CAT I approaches are 1×10^-4 for continuity and 3.5×10^-7 for integrity per approach.

These requirements are allocated between aircraft and non-aircraft subsystems.

• Aircraft subsystems include all onboard navigation components, such as the GNSS receiver hardware, timing modules and processing software. Failures originate from internal causes (hardware faults, power interruptions, interface failures). Compliance with the continuity and integrity requirements is the responsibility of the aircraft manufacturer or avionics supplier, who must demonstrate their equipment satisfies the allocated risk budgets. In certification, continuity compliance is commonly shown using Mean Time Between Failure (MTBF) analysis, whereas the integrity requirement may be validated through design assurance processes and fault detection mechanisms as defined by applicable certification standards.

• Non-Aircraft subsystems correspond to external contributors affecting navigation performance. In an SBAS-augmented architecture, this branch is limited to SIS, including GNSS signals and SBAS corrections. The navigation system must, therefore, ensure compliance with these requirements through appropriate integrity monitoring. Because these non-aircraft requirements relate solely to the external environment, the on-board equipment, specifically the GNSS receiver, are assumed to be ideal (or fault-free), i.e., operating nominally without introducing failures within the measurements. Under this assumption, responsibility for meeting the allocated performance requirements resides with the on-board navigation system, specifically through its integrity monitoring algorithms. Consequently, the non-aircraft continuity and integrity requirements define the performance thresholds the navigation system must meet.

Continuity Requirements for the Vision-Integrated Navigation System

Following the same methodology, the vision subsystem is decomposed into aircraft and non-aircraft branches to clearly delineate responsibility boundaries. This classification enables the identification of risks that fall under the scope of the aircraft manufacturer versus those that must be addressed by the on-board navigation monitoring functions.

Vision Aircraft Loss of Continuity 

Aircraft continuity risks originate from failures of onboard hardware or software involved in the vision processing chain. Vision measurements are produced through two main stages: image acquisition by optical sensors and landmark detection using onboard image-processing algorithms.

Failures affecting either stage may interrupt the generation of vision measurements. Optical sensors can be affected by hardware faults such as lens contamination, power interruption or optical degradation, while the processing chain may suffer from processor failures or software crashes. In this study, an aircraft-level continuity loss is defined as any failure of the onboard vision subsystem to produce a runway landmark measurement, assuming the scene observability allows it.

The continuity requirement allocated to the vision function is 10^-1 per approach. This relatively relaxed constraint reflects common image degradation mechanisms such as lens contamination or water droplets. Compliance is verified through equipment reliability analysis (e.g., MTBF), and redundancy such as sensor triplication can be used to improve overall continuity performance.

Vision Non-Aircraft Loss of Continuity 

Non-aircraft continuity risks correspond to environmental effects that degrade vision measurements while the onboard equipment operates nominally. In this context, the vision subsystem is assumed to produce at least one valid measurement. Under this assumption, continuity loss may occur when the navigation system monitoring declares an alarm, for instance when protection levels exceed the alert limits or when a measurement anomaly cannot be excluded.

For GNSS, environmental disturbances are captured within the SIS concept. In vision-based navigation, the equivalent disturbances arise from the optical environment, which affects the propagation of visible or infrared radiation between the runway and the camera. Environmental perturbations increasing measurement noise are generally referred to as photometric noise, and include poor illumination conditions or strong reflections from the runway surface. These effects increase measurement variance and protection levels, whereas large biases or outliers are addressed within the integrity monitoring framework.

For the hybrid navigation system, the non-aircraft continuity risk is allocated to 8×10^-5 per approach. 

Weather Impact

Operational conditions may prevent the vision subsystem from producing any measurement, for instance during night operations with visible-spectrum cameras or under adverse meteorological conditions. Such situations must be explicitly considered in the continuity allocation.

In this study, meteorological conditions preventing vision measurements are classified as non-aircraft continuity risks, as they originate from the external sensing environment rather than from failures of the onboard equipment. This treatment is consistent with the modeling of GNSS outages caused by radio frequency disturbances.

Two modeling strategies can be considered. One approach assumes complete vision unavailability due to environmental conditions is negligible compared to continuity losses caused by monitoring false alarms. However, this assumption is unrealistic because no existing optical system can guarantee a negligible probability of total vision unavailability.

The adopted approach, therefore, explicitly accounts for weather effects by decomposing the vision observation continuity risk into two contributions:

• Losses caused by adverse meteorological conditions, and

• Losses caused by false alarms of the fault detection function.

Assuming one approach out of 20 is affected by weather conditions preventing optical measurements, the resulting continuity risk associated with vision observation is 3×10^-2 per approach. For a fault detection rate of 1 Hz, this corresponds to a false alarm probability of

Continuity Risk Allocation Tree

The introduction of vision into an inertial-GNSS navigation architecture affects both aircraft level equipment continuity risks and non-aircraft continuity risks driven by the external environment. The corresponding allocation tree is illustrated in Figure 3. In this representation, scene observation is explicitly placed within the non-aircraft domain, as it inherently accounts for environmental effects, including meteorological conditions. The aircraft-level vision function is represented by its two main components: the optical sensors and the image-processing unit. 

The introduction of vision-based navigation substantially alleviates the continuity requirements previously imposed on the GNSS Signal-in-Space. In both aircraft and non-aircraft contexts, continuity loss occurs only when vision and GNSS are simultaneously unavailable. This architectural change yields multiple benefits. First, it relaxes equipment-level continuity requirements, which is advantageous for both aircraft manufacturers and equipment suppliers. Second, it explicitly addresses the growing risk of radio frequency interference, as the continuity risk allocated to the GNSS SIS is reduced by a factor of 12.5, down to 1×10^-3 per approach.

Integrity Requirements for Vision-Integrated Navigation 

Vision Aircraft Loss of Integrity

Quantifying the integrity associated with airborne vision equipment is challenging. Integrity failures associated with airborne vision equipment occur when erroneous measurements produced by the onboard vision subsystem are accepted as valid by the navigation system and lead to navigation errors exceeding the alert limits. As with continuity risks, compliance with integrity requirements is primarily ensured through equipment certification.

Aircraft-level integrity threats originate from two components of the vision subsystem:

• Optical sensors may experience hardware failures such as calibration errors, lens defects, geometric distortions, or failures of the imaging elements.

• Image processing failures arise from abnormal behavior of the onboard processing chain, including feature detection errors, computing faults, radiation-induced bit errors, or errors in optical multi-sensor fusion. Because the integrity risks considered in the aircraft domain are related to equipment failures rather than external environmental conditions, a core assumption is adopted: In the absence of sensor or processing failures, the produced measurement would be correct.

Failures affecting optical sensors can reasonably be considered random and statistically independent, i.e., not subject to common-mode effects. Under this assumption, and in addition to integrity loss rates guaranteed by the manufacturer through certification processes, these integrity risks can be mitigated through a combination of equipment redundancy, and internal fault detection mechanisms within the processing chain. 

These mitigation strategies may reduce the integrity loss probability associated with airborne vision equipment to levels that are either negligible (≈10^-9 per approach) or sufficiently small to remain within the aircraft-level integrity allocation already assigned to the inertial-GNSS navigation system (10^-7 per approach). Whether a specific allocation should be explicitly assigned to vision equipment remains open to interpretation. Regardless of the chosen allocation strategy, the validation and certification of vision equipment integrity remain the responsibility of the equipment manufacturer, as these failure modes are not monitored by the fault detection mechanisms implemented at the navigation system level.

Vision Aircraft Loss of Integrity 

A non-aircraft integrity failure occurs when a vision measurement is corrupted by abnormal errors induced by the external environment. In contrast with continuity analysis, measurement availability is assumed, and environmental effects are considered only through their impact on measurement quality.

The visual environment along the line of sight between the runway and the onboard camera plays a central role. Measurement errors generally consist of two components:

• Photometric noise, representing the nominal stochastic error of the measurement, and

• Deterministic biases, corresponding to abnormal measurement errors.

Photometric noise arises from variations in illumination conditions or scene characteristics, such as overexposure, motion blur, atmospheric disturbances, or runway reflections. Although these effects may increase measurement variance and degrade navigation accuracy, they are treated as nominal realizations within the measurement noise model. The corresponding feared event arises when the photometric noise magnitude becomes abnormally large, corresponding to extreme realizations in the tails of the assumed Gaussian distribution. Although such events may significantly affect navigation accuracy, they are considered as rare normal performance under fault-free conditions, as no underlying abnormal failure or bias is present.

Integrity-threatening events correspond to deterministic biases affecting the estimated line of sight between the landmark and the camera. Two main sources of such biases are identified:

• Incorrect feature detection, where the selected landmark does not belong to the intended runway.

• Incorrect landmark association with the corresponding three-dimensional reference coordinates.

Preliminary studies have proposed models for nominal measurement errors [16] and landmark association failures [12]. However, these results remain limited to specific scenarios and do not yet satisfy the stringent integrity requirements of civil aviation. Consequently, conservative assumptions are typically adopted when modeling vision-based integrity risks.

On-Board Monitoring Assumptions

The use of two parallel navigation solutions enables a clear dissociation between integrity risks associated with GNSS and those associated with vision-based navigation. Depending on which navigation branch is active, Main Navigation or Vision Navigation, the set of measurements used to compute the navigation solution differs. As a result, the corresponding failure events, namely GNSS SIS failure and vision observation failure, are mutually exclusive and cannot be jointly considered within a single integrity allocation tree. Each navigation solution is therefore characterized by its own failure modes, its own fault tree, and a dedicated integrity monitoring strategy.

Given that it is not possible to determine with certainty in advance which of the two navigation solutions will be active during a given approach, a conservative assumption is adopted. Accordingly, the full integrity risk associated with the on-board navigation system monitoring, equal to 2×10^-7 per approach, is allocated to each navigation solution without assuming any prior knowledge of the active one.

Main Navigation Integrity Risk Allocation

For the Main Navigation, because it corresponds to a state-of-the-art inertial-GNSS system augmented by SBAS, the sole failure mode to be considered is the SIS failure. The associated integrity monitoring is based on two hypotheses when this navigation branch is active:

H₀ Fault-Free: An HMI event may arise due to excessive measurement noise on the pseudo range observations.

H₁: Signal-In-Space Failure: One or more satellite measurements are faulty, or the ground segment is corrupted.

The total integrity risk allocated to the navigation system (2×10^-7 per approach) is therefore distributed equally between the two navigation hypotheses of the Main Navigation. The integrity of this navigation configuration is well established in the literature. In particular, the integrity risk allocation tree proposed by [15] can be directly applied, together with standard GNSS integrity monitoring techniques. As a result, the dual-navigation architecture avoids imposing additional integrity constraints on the GNSS SIS performance.

Vision Navigation Integrity Risk Allocation

In the case of Vision Navigation, only one failure mode is considered: Vision Observation Failure mode. When this navigation is active, integrity monitoring is based on two hypotheses:

H₀ Fault-Free: An HMI event may result from excessive photometric noise induced by the observed scene. 

H₁ Vision Observation Failure: A measurement bias affects one or more visual landmarks.

The entire integrity risk of the navigation system (2×10^-7 per approach) is distributed between the fault free and faulty vision hypotheses based on an arbitrary allocation. In this study, the integrity risk associated with the fault-free hypothesis is set to 4×10^-8 per approach, while the risk allocated to the Vision Observation Failure mode is set to 1.6×10^-8 per approach.

Probability of Missed Detection 

Based on the proposed allocations, it is possible to derive the missed detection probability required for a fault detection algorithm applied to vision measurements, defined as 

Assuming the correlation time of a vision failure and its associated integrity loss extend over the entire approach duration, the required missed detection probability is given by [14]

where IR_req denotes the integrity requirement associated with a vision observation failure, and R(H₁) represents the occurrence rate of vision observation failures. 

This expression highlights that the missed detection probability is inherently linked to the occurrence rate of vision observation failures. However, accurately quantifying this rate remains challenging. Unlike GNSS, vision-based sensors do not benefit from several decades of operational experience and extensive user feedback, particularly in the aeronautical domain. To mitigate this uncertainty, internal consistency checks within the vision processing pipeline, such as image filtering, plausibility tests, or redundancy-based consistency checks, may be implemented to reduce the effective vision failure rate. In addition, the use of external position estimates can constrain the search area for the runway within the image, thereby improving robustness.

A failure probability of 10^-4 for vision-based observations has been suggested in [9]. Assuming a correlation time equal to the approach duration, this corresponds to a failure rate of 10^-4 per approach. In this study, a slightly more conservative value of 1.6 ×10^-4 per approach is adopted, leading to a maximum allowable missed detection probability of

Dual-Navigation Integrity Risk Tree

The modified integrity allocation tree for the dual-navigation system is shown in Figure 4. The non-aircraft allocation consists of two separate subtrees: one for the Main Navigation and one for the Vision Navigation. The navigation system selects the relevant subtree depending on the active navigation mode. This behavior is represented by a switching element at the top level of the integrity allocation tree. Consequently, the total non-aircraft integrity risk is allocated independently to each navigation branch.

Conclusion

This study investigated the role of vision-based measurements in improving the continuity of navigation services for civil aircraft during precision approach operations. A continuity risk allocation tree was developed to analyze the contribution of vision sensors while distinguishing between aircraft-level equipment failures and observation failures at the navigation system level.

To accommodate the specific characteristics of vision measurements, a dual-navigation architecture was proposed. In this architecture, the navigation system operates with an SBAS-augmented inertial-GNSS solution when GNSS signals are available and transitions to an inertial-vision solution when GNSS is declared unavailable. This design enables the separation of GNSS and vision constraints and leads to the definition of two independent integrity allocation trees corresponding to the two navigation modes.

The proposed framework contributes to the design of resilient navigation architectures capable of maintaining navigation service during GNSS outages. The integrity constraints associated with the vision-based navigation mode were analyzed, and the corresponding false alarm and missed detection probability requirements were derived. These results provide key guidelines for developing dedicated fault detection and integrity monitoring algorithms for vision-based navigation systems intended for safety-critical aviation applications. 

Acknowledgements 

This article is based on material presented in a technical paper at ION GNSS+ 2025, available at ion.org/publications/order-publications.cfm.

References 

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(2) ICAO, Annex 10. Aeronautical Telecommunications. Vol. 1. Radio Navigation Aids., 2023. 

(3) FAA, “Provision of Navigation Services for the Next Generation Air Transportation System (NextGen) Transition to Performance-Based Navigation (PBN) (Plan for Establishing a VOR Minimum Operational Network),” 2016.

(4) Y. Watanabe, A. Manecy, A. Hiba, S. Nagai and S. Aoki, “Vision-integrated navigation system for aircraft final approach in case of gnss/sbas or ils failures,” AIAA Scitech 2019 Forum, p. 0113, 2019. 

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(8) C. Tonhäuser, A. Schwithal, S. Wolkow, M. Angermann and P. Hecker, “Integrity concept for image-based automated landing systems,” Proceedings of the ION 2015 Pacific PNT Meeting, pp. 733–747, 2015. 

(9) H. Jiang, T. Li, D. Song and C. Shi, “An effective integrity monitoring scheme for gnss/ins/vision integration based on error state ekf model,” IEEE Sensors Journal, pp. 7063–7073, 2022. 

(10) C. Zhu, M. Joerger and C. Günther, “Integrity of visual navigation—developments, challenges, and prospects,” NAVIGATION: Journal of the Institute of Navigation, p. 69(2), 2022. 

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Authors

Gabriel Thys is a Ph.D. candidate at Safran Electronics & Defense in collaboration with ENAC. His research focuses on GNSS, vision-based navigation, inertial systems, multi-sensor fusion, and integrity monitoring algorithms. He obtained a M.Eng. degree in space telecommunications from ENAC . He works as a system engineer in signal processing for high-performance aeronautical navigation systems at Safran Electronics & Defense.

Christophe Macabiau graduated as an electronics engineer in 1992 from the ENAC. Since 1994, he has worked on the application of satellite navigation techniques to civil aviation. He received his Ph.D. in 1997 and has been in charge of the signal processing lab of ENAC since 2000. He is the head of the TELECOM research team of ENAC that includes various research groups.

Raphael Jarraud is a senior expert in inertial navigation and sensor fusions, working for Safran Electronics & Defense. He has 22 years of experience in designing, simulating and testing inertial navigation systems. He graduated from CentraleSupelec in 2003, with a major in control systems.

Julien Lesouple received the Eng. degree in Aeronautics Engineering from ISAE Ensica, Toulouse, France in 2014 and his Ph.D. in Signal Processing from Toulouse Institut National Polytechnique in 2019. Since 2021, he has worked as an Associate Professor at ENAC within the SIGNAV team. His research interests include statistical signal processing, machine learning, estimation and detection theory, filtering, with applications to satellite communications, localization, tracking, navigation, and anomaly detection.

Jérémy Vézinet graduated as an electronics engineer in 2010 and obtained his Ph.D. in 2014 on multi-sensor hybridization from ENAC. He has worked as a Research Associate in the TELECOM Research Team at ENAC since 2014. His interests are GNSS, INS, video-based navigation, multi-sensor hybridization and integrity monitoring.

Anaïs Martineau graduated in 2005 as an electronics engineer from the ENAC. Since 2005, she has worked at the signal processing lab of the ENAC, where she carries out research on integrity monitoring techniques. She received her Ph.D. from the Université de Toulouse. She is the head of Electronics, Electromagnetism and Signal Processing Division and ENAC Engineers and GNSS Master’s Course Director.

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GOSAS: From Accidental Discovery to Dedicated Mission

The GNSS Orbiting Situational Awareness Sensor, or GOSAS, is a CubeSat-compatible, programmable dual GPS receiver that will operate from orbit to monitor conditions affecting space-based GNSS signals. “Understanding and predicting space weather is critical for ensuring the accuracy of GPS and the integrity of military communications,” said Scott Budzien, NRL research physicist and GOSAS principal investigator.

GOSAS is a direct follow-on to NRL’s GROUP-C experiment, which operated aboard the International Space Station from 2017 to 2023. GROUP-C’s primary mission was GPS radio occultation and ultraviolet photometry, but the experiment serendipitously detected GPS ground interference from orbit — a finding with significant implications for counterspace situational awareness. GOSAS was conceived in 2020 specifically to build on that capability, formalizing orbital GNSS environment characterization as a dedicated mission objective rather than an incidental one. The timing is notable: as documented in assessments including the Secure World Foundation’s newly released Global Counterspace Capabilities 2026, GPS jamming over conflict zones has now been shown to affect LEO satellites carrying onboard GPS receivers, creating measurable gaps in orbital PNT coverage. A purpose-built sensor for detecting and characterizing that interference environment addresses a documented and growing operational gap.

The STPSat-7 spacecraft launched at approximately 4:33 a.m. PDT from Vandenberg Space Force Base, California, aboard a Northrop Grumman Minotaur IV launch vehicle as part of the STP-S29A mission.

Companion Payloads Address Debris and Radiation Detection

The two additional NRL payloads round out a broad space environment characterization effort. LARADO — the Lasersheet Anomaly Resolution and Debris Observation instrument — will detect and characterize small orbital debris that cannot be tracked from the ground, providing data to update debris models used by spacecraft engineers, satellite operators, and policymakers. The LARADO concept dates to 2012 and has been funded since FY22 through NASA’s Heliophysics Division. GARI-1C, the third payload, will space-qualify new gamma-ray detector technology using commercial off-the-shelf components, with an eye toward future defense applications including detection of weapons of mass destruction from orbit.

The Space Test Program, operating under U.S. Space Systems Command, was established in 1966 to provide flight opportunities for research and development payloads with potential military utility. “The success of this mission highlights how cutting-edge research and development are fundamental to preserving America’s strategic edge in space,” said USSF Lt. Col. Brian Shimek, system program manager and director for STP.

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