The four-year contract, which began March 31, 2026, will advance GNSS radio occultation (GNSS-RO), polarimetric radio occultation (GNSS-PRO), and reflectometry (GNSS-R) capabilities. 

As the largest commercial provider of GNSS-RO data, PlanetiQ currently operates a global constellation of satellites equipped with advanced receivers capable of capturing high signal-to-noise-ratio GNSS-RO and GNSS-PRO measurements. GNSS-PRO has demonstrated strong efficacy for measuring precipitation, a key capability for improving severe weather forecasting. 

The STRATFI award extends that foundation in two directions. PlanetiQ will refine data-assimilation techniques to integrate GNSS polarimetric radio occultation data into numerical weather models, which improves the characterization of precipitation. The next-generation receiver will also add GNSS-R capabilities, supporting new applications such as ocean surface wind measurement, sea state characterization, and soil moisture monitoring over land. Data delivered will support Air Force applications including AI model training, data assimilation, and performance evaluation.

“This award is a big indication from the U.S. government that our technology matters and they are willing to put $15 million toward it,” said Chris McCormick, PlanetiQ chairman and founder. CEO Ira Scharf added that combining the three measurement types in a single platform would unlock “a more complete picture of the atmosphere and Earth’s surface.”

The Air Force contract builds on a string of government data agreements. In September 2025, NOAA awarded PlanetiQ a $24.3 million contract under the Commercial Data Program’s Radio Occultation Data Buy 2 — the agency’s single largest commercial satellite weather data purchase. Under that agreement, PlanetiQ delivers 7,000 GNSS-RO profiles per day, including 500 enhanced high-SNR profiles described as more than seven times higher in quality than profiles from other commercial providers, along with 2,500 low-latency Total Electron Content tracks daily. While NOAA is the procuring agency, the data is also used by NASA, the U.S. Air Force, the U.S. Navy, and international government weather agencies. 

The STRATFI program is administered through AFWERX, the innovation arm of the Department of the Air Force and a directorate within the Air Force Research Laboratory, which has awarded more than $7.24 billion in contracts since 2019 to accelerate technology transition to operational capability. 

PlanetiQ was founded in 2015 by McCormick, who previously led Broad Reach Engineering, a pioneer in GPS radio occultation sensors for missions including COSMIC, before its acquisition by Moog in 2012. 

The post PlanetiQ Lands $15M Air Force STRATFI Contract for Next-Generation GNSS Weather Constellation appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

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.

References 

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(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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The HydroGNSS mission—ESA’s first Earth Observation “Scout” mission to reach orbit—launched from Vandenberg Space Force Base in California in November 2025 and consists of two small satellites designed to monitor key hydrological and climate variables using signals from global navigation satellite systems (GNSS). 

EECL supplied six multiband ultra-low-noise microwave amplifiers (LNAs) that form part of the radio-frequency front end for the mission’s GNSS reflectometry receiver. The LNAs amplify extremely weak reflected navigation signals while preserving signal integrity, allowing the satellites to capture usable data at the earliest stage of signal reception. 

HydroGNSS uses a technique known as GNSS reflectometry, in which satellites receive navigation signals from systems such as GPS and Galileo after they bounce off Earth’s surface. By analyzing those reflections, the spacecraft can derive environmental measurements including soil moisture, freeze–thaw conditions over permafrost, inundation and wetlands, and above-ground biomass. 

Early commissioning results indicate the payload hardware is performing as expected. Both satellites have successfully begun collecting Delay Doppler Maps—datasets that characterize the reflected GNSS signals and allow scientists to extract environmental information about the reflecting surface. 

The LNAs were designed, manufactured and tested in the United Kingdom under a contract with Surrey Satellite Technology Ltd. (SSTL), which built the satellites and the GNSS receiver payloads. Their in-orbit performance validates the RF hardware after several years of development and space-qualification testing. 

Low-noise amplification is particularly critical for GNSS reflectometry missions because the reflected navigation signals arriving at the satellite are extremely faint compared with direct signals from the GNSS satellites themselves. Maintaining a very low noise figure in the front-end electronics enables the receiver to detect these weak reflections and generate usable scientific data products. 

HydroGNSS will collect global measurements of hydrological conditions to support climate monitoring and environmental research. According to ESA, the twin satellites operate in complementary orbital positions to maximize global coverage while continuously gathering reflected GNSS signals for analysis.

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Organizers and police for Finnmarksløpet, Europe’s longest sled dog race, say ongoing jamming and spoofing are degrading the GPS trackers carried by each team, forcing the event to lean more heavily on trail marking and traditional navigation.

Race tracking hit by jamming and spoofing

According to reporting in the Barents Observer, Norway’s Finnmark police and race officials have confirmed that GNSS disturbances are affecting the race’s live tracking system. Each sled in Finnmarksløpet carries a GPS device so organizers, safety teams, and the public can follow progress across the Finnmark plateau. Military jamming and spoofing from Russia’s Kola Peninsula are now interfering with both reception and transmission of those signals. 

Tarjei Sirma-Tellefsen, Chief of Staff at the Finnmark Police District, said police are in “good dialogue” with the race regarding participant safety but “unfortunately see GNSS disturbances occurring in the area.” In practice, that can cause sled positions to freeze, jump erratically, or appear in the wrong place altogether on the public map.

The race route runs from Alta east across the Finnmark plateau to Kirkenes, near the Russian border, and back again. Portions of the course follow the Pasvik valley along the western shore of the river that separates Norway from Russia’s Kola Peninsula – placing mushers and their GPS equipment squarely inside a region that has seen repeated interference over the past several years. 

Part of a broader High North interference pattern

Finnmarksløpet is the latest in a series of civilian activities in Norway’s far north affected by Russian GNSS interference.

Norwegian authorities first reported systematic jamming impacting aviation and other GPS users in eastern Finnmark in 2017. District police at the time described outages as frequent enough to be considered “the new normal,” requiring long-term planning for degraded GPS. 

Since then, pilots approaching Kirkenes and other northeastern airports have reported near-daily GNSS interference, with signals distorted or lost on approach and alternative navigation systems such as inertial and ground-based aids used as primary references. 

Further south, Finland and Estonia have issued navigation warnings for the Gulf of Finland due to persistent GNSS disruption traced to Russian and Belarusian territory, citing increased risk to shipping and a need for mariners to treat satellite navigation with caution. 

Taken together, the incidents show a broad arc of GNSS interference stretching from the Arctic High North down into the Baltic – with the dog-sled race now providing a very public, human-scale illustration of the problem.

A live test of “everyday” PNT resilience

Finnmarksløpet is a reminder that satellite navigation is now embedded in activities far beyond aviation, shipping, or defense.

In this case:

• Each team’s GPS tracker underpins safety monitoring, media coverage, and fan engagement.
• Organizers, rescue teams, and family members rely on those positions to confirm that mushers and dogs are on course and moving as expected in harsh winter terrain.
• When jamming or spoofing degrades those signals, race control has to fall back on more traditional tools: marked trails, checkpoints, radio communications, and map-and-compass navigation for participants. 

From a PNT resilience standpoint, the situation checks several familiar boxes:

• Single-sensor dependence: GPS trackers are often built around L1-only receivers with limited interference detection.
• Lack of redundancy: consumer-grade tracking platforms may not fuse inertial sensors, terrestrial beacons, or multi-constellation, multi-frequency GNSS in ways that help detect spoofing or jamming.
• Human expectations: fans and even some safety stakeholders may assume that a public tracking map is authoritative, when in fact it may be running on degraded or manipulated data.

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New research supported by the European Space Agency’s (ESA) Third Party Missions programme has generated Arctic-wide sea ice freeboard maps using GNSS-Reflectometry (GNSS-R) data captured by Spire Global, Inc.’s GNSS-Reflectometry (GNSS-R) multipurpose listening constellation.

Led by the Technical University of Munich (DGFI-TUM) and the Norwegian Research Centre, the study leveraged Spire’s grazing-angle GNSS-Reflectometry (GNSS-R) — a radio frequency (RF) sensing technique that analyzes reflected navigation signals — to retrieve sea ice freeboard measurements across an entire winter season. The results show strong alignment with established altimetry datasets, including ESA’s CryoSat mission, validating the complementary role of commercial satellite data alongside government missions.

While GNSS signals have long been used for positioning, this research highlights how reflected signal analysis can extend their value into large-scale Earth observation applications, delivering persistent coverage independent of sunlight or weather conditions.

“Advances in miniaturization, digital signal processing, and machine learning have fundamentally changed what’s possible in RF sensing,” said Theresa Condor, Chief Executive Officer of Spire Global. “Commercial constellations can now deliver persistent, high-quality RF data that complements traditional government systems with greater flexibility and cost efficiency. As environmental monitoring requirements intensify, we’re seeing agencies increasingly integrate commercially sourced RF datasets into operational architectures, reflecting the continued maturation of this market and the growing role of commercial infrastructure in government missions.”

Read more on the research from ESA: Reflected satellite signals unlock new insights into Arctic sea ice

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As world leaders assemble for the 30th Conference of the Parties (COP30) to the United Nations Framework Convention on Climate Change (UNFCCC) in Brazil, UK Prime Minister Keir Starmer warned that the international “consensus is gone,” on fighting climate change, signaling a political environment more fractured than at any point since the 2015 Paris Agreement. Yet, as geopolitical alignment weakens, the European GNSS community is readying a mission designed to deliver precisely the kind of objective, physics-based data that can underpin global climate action.

HydroGNSS comprises two identical small satellites flying in low Earth orbit (LEO) at roughly 550 km, positioned 180 degrees apart to maximize global revisit. Each spacecraft carries a GNSS reflectometry (GNSS-R) payload engineered to capture both direct and Earth-reflected L-band signals from Galileo, GPS, BeiDou, and GLONASS.

By comparing the properties of the reflected waveforms, such as delay, Doppler shift, phase coherence, and signal-to-noise ratio, the system derives geophysical parameters with resilience to cloud cover, limited sunlight, and radio-frequency variability. This makes GNSS-R particularly well suited for hydrological monitoring in regions where traditional sensors face constraints.

The mission focuses on four climate-critical variables: soil moisture, freeze-thaw state, inundation extent, and above-ground biomass. Soil-moisture retrievals support drought forecasting, precision-agriculture models, and wildfire-risk assessments. Freeze-thaw mapping contributes to monitoring permafrost integrity, a key uncertainty in carbon-feedback projections. Wetland-inundation observations help quantify methane emissions and track floodplain evolution. Biomass estimates provide independent constraints on terrestrial carbon stocks, complementing optical and radar-based methods.

Key climate insights

The political significance of HydroGNSS is difficult to overstate. As Starmer cautions that diverging national priorities threaten coordinated climate action, HydroGNSS will offer a globally consistent and well-calibrated data stream. Unlike negotiated pledges, GNSS-R observations cannot be distorted by political framing: if soil-moisture regimes shift, if thaw boundaries advance, if wetlands decline, HydroGNSS will record the change with uniform methodology across all continents.

For ESA and the GNSS community, the mission also showcases the value of the Scout-class model, i.e. rapid-development, cost-capped missions designed to adapt innovative techniques into operational tools. If HydroGNSS performs as expected, it will strengthen the case for future GNSS-R constellations capable of delivering near-real-time hydrological intelligence.

As COP30 exposes shifting political winds, HydroGNSS’s global, physics-driven data record will help ensure that climate-related decisions remain grounded in observable reality. HydroGNSS is being developed under ESA’s FutureEO programme, within the Scout-class missions designed for rapid, low-cost Earth observation innovation.

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Swift Navigation, a leader in precise positioning technology, today announced that its Swift Partner Program has grown to include over 20 GNSS receiver manufacturers, establishing it as the largest hardware ecosystem among GNSS correction providers. This milestone, achieved less than two years after the program’s launch in October 2023, is accelerating the adoption of precise positioning in mass-market applications across automotive, robotics, mobile, and mapping.

Centimeter-accurate, reliable, and cost-effective positioning technologies are key to unlocking vehicle autonomy, industrial automation, and next-generation location-based mobile applications. However, integrating precise positioning can add complexity and cost to the product design cycle, which can delay product launches and lead to suboptimal user experiences. By enabling precise positioning at scale, Swift Navigation and its partners are accelerating the deployment of autonomy and automation across industries.

“Reaching this milestone with over 20 partners is a powerful validation of our ecosystem-first strategy,” said Holger Ippach, Executive Vice President of Product and Marketing at Swift Navigation. “We believe that by creating an open and collaborative platform, we empower our customers to select the best hardware for their needs, streamline their design process, and future-proof their investments. This success is a shared one, and it highlights how our collective efforts are making precise positioning more accessible and scalable than ever before.”

Ecosystem-Driven by Design

Swift’s ecosystem approach is redefining how precise positioning scales by enabling correction delivery across the industry’s broadest hardware base, from chipsets to complete systems. This receiver-agnostic strategy provides customers with a wide range of interoperable components and options for any stage of the design cycle. The Swift Partner Program solidifies Swift’s leadership among correction service providers, offering unmatched interoperability and accelerating time-to-market for OEMs and device makers.

Customers can maintain complete control over their hardware roadmap, selecting the best components for their needs without being tied to a proprietary corrections stack. The Skylark™ Precise Positioning Service’s receiver-agnostic architecture supports integration at every level of the technology stack, giving partners the flexibility to build low-power modules, multi-frequency systems, or full-featured receivers.

Swift’s collaborative method spans the entire OEM lifecycle, including:

• Co-defining precision targets from the outset.
• Validating designs through joint testing of early samples in labs.
• Rigorously field-testing in real-world use case conditions to ensure performance at scale.

This positions Swift as a key infrastructure layer for precise positioning that is hardware-agnostic, scalable, and capable of supporting mass adoption across various industries.

Built-in Customer Benefits

Swift’s ecosystem approach delivers several key benefits to customers:

• Streamlined Design: Provides a wide array of interoperable components, allowing developers to optimize for performance, cost, and footprint, and even retrofit existing systems.
• Minimized Costs: Customers have access to multiple hardware vendors and flexible pricing options, which minimizes costs and avoids lock-in.
• Accelerated Integration: Deep OEM collaboration, rigorously tested mass deployed components, and joint debugging reduce integration risk and accelerate the integration process.

Continuous Innovation & Future-Proofing

Swift’s technology is designed to evolve quickly to keep customers ahead.

• Future-Proof Receiver Investment: Skylark can ingest new satellites and signals as they launch, maximize the precision of quad-frequency receivers, and boost the accuracy of cost-effective receivers.
• Continuous Improvements: Swift continuously expands coverage based on customer needs and uses machine learning to improve accuracy and adapt to atmospheric variability in real time.
• Freedom to Evolve: Partners can switch, upgrade, or expand their hardware without changing their corrections pipeline.

Among the more than 20 GNSS receiver manufacturers in the Swift Partner Program, the following partners commented on their collaborations with Swift. Each offers Skylark-compatible products—ranging from chipsets and modules to complete GNSS receivers, smart antennas, and integrated systems.

Skylark-Compatible Chipsets

Sony Semiconductor Solutions
“Our collaboration with Swift Navigation brings high-accuracy positioning to compact GNSS devices using our low-power, high-performance CXD5610GF GNSS receiver IC. Seamless compatibility with Skylark enables developers to integrate precise positioning into wearables, mobile trackers, and other space-constrained applications—while maintaining multi-day battery life in continuous operation,” said Kenichi Nakano, General Manager, Analog LSI Business Division, GNSS Product Dept. at Sony Semiconductor Solutions.

STMicroelectronics
“Close collaboration with Swift Navigation during the development phase of our new TeseoVI chipset has produced a very high performance GNSS platform that integrates seamlessly with Skylark, and is tailored for automotive safety and autonomy,” said Luca Celant, General Manager, Digital Audio and Signal Solutions Division at STMicroelectronics. “Optimized for ADAS L2+ and autonomous driving, the integrated solution streamlines system integration, cuts cost, accelerates time-to-market, and delivers lane-level accuracy essential for next-generation driver assistance and autonomy.”

Skylark-Compatible Modules

Quectel
“Quectel is dedicated to delivering high-performance positioning solutions to our customers. By integrating Skylark’s advanced GNSS corrections with our high-precision modules, such as the LG290P and LC29H, we are empowering developers with flexible, cost-effective options to bring centimeter-level accuracy to applications in intelligent driving, robotics and micromobility with reliable performance across Skylark’s extensive coverage area,” said Brandon Oakes, Director, GNSS, Short Range and Channel, Quectel Wireless Solutions.

Septentrio
“Skylark unlocks the full potential of our mosaic and AsteRx receivers, combining multi-constellation, multi-frequency performance with robust interference resilience,” said Jan Van Hees, Business Development Vice President at Septentrio. “This gives our customers the confidence to deploy centimeter-accurate positioning in demanding applications such as robotics, surveying, and autonomous systems.”

Telit Cinterion
“Swift’s Skylark integration brings real‑time RTK corrections directly into our SE868K5-RTK and SE868K5-DR modules—enabling centimeter‑level accuracy for precision agriculture, drone operations, asset tracking, and other high‑value IoT applications,” said Marco Argenton, Senior Vice President of Product Management, IoT Modules at Telit Cinterion. “Whether in open skies or GPS-challenged environments like urban canyons or underground structures, our modules—coupled with Skylark—deliver unmatched positioning performance with minimal power consumption in a compact form factor, and achieve centimeter-level accuracy within seconds.”

Skylark-Compatible Receivers

Bad Elf
“Skylark’s broad and continuous coverage gives our Flex and Flex Mini customers the confidence to operate in geographies that typically aren’t served,” said Larry Fox, Vice President of Marketing and Business Development at Bad Elf. “With continental coverage across North America, Europe, and large parts of Asia-Pacific, customers know they’ll get consistent, real-time centimeter-level accuracy with high reliability, wherever they go.”

Calian
“Through our collaboration with Swift, we now offer Skylark-ready smart antennas that have been rigorously tested for performance and reliability,” said Christopher Russell, Vice President of Sales for Calian GNSS. “Together, we deliver high-precision positioning that customers can trust for applications such as navigation, driver safety, robotics, and UAVs—while dramatically reducing integration time.”

Emlid
“By pairing Swift’s Skylark Precise Positioning Service with our lightweight and rugged Reach receivers, we are delivering a turnkey surveying and mapping solution that’s easy to deploy, cost-effective, and capable of achieving centimeter-level accuracy in seconds—even in challenging environments,” said Igor Vereninov, Co-founder and CEO of Emlid.

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HydroGNSS is one of a series of ESA missions, the so-called Scout missions, part of the agency’s FutureEO program, designed to quickly and cheaply demonstrate new Earth observation techniques using small satellites.

GNSS signals are differentially reflected or scattered by the Earth’s surface, as affected by water content, specifically permittivity, surface roughness and overlying vegetation. Once analyzed, these reflected signals can provide information about various geophysical properties. Special innovations introduced by HydroGNSS are to include dual-polarization and dual-frequency (L1/E1 and L5/E5) reception, and collection of high-rate coherent reflections.

Compact but powerful Earth observation platform

HydroGNSS uses the SSTL-21 platform, measuring 45 cm x 45 cm x 70 cm and weighing around 65 kg total per satellite. The payload will be operated at near 100% duty, and can support high data download rates using an X-Band transmitter. Star cameras provide precise attitude measurements, and a xenon propulsion system permits orbit phasing, collision avoidance and supports satellite disposal at the end of the mission. The two HydroGNSS satellites will take a ride-share launch into a 550 km sun-synchronous orbit, phased apart by 180 degrees to maximize coverage.

The SGR-ReSI-Z payload is a delay Doppler mapping receiver, tracking the direct GPS and Galileo signals through a zenith antenna and processing the reflected signals from a nadir antenna to create delay Doppler maps (DDMs). The zenith and nadir antennas employ all-metal patch technology, enabling the reception of dual-frequency and dual-polarized signals. Low noise amplifiers include blackbody loads to provide calibration for the amplitude measurement. Generated measurement datasets can be stored in the satellite’s data recorder and downloaded to ground stations at allocated passes several times per day.

Speaking at his annual press briefing in Paris earlier this month (January 2025), ESA Director General Joseph Aschbacher said, “We now expect to launch HydroGNSS in the fourth quarter of 2025, as one of the three so-far-identified Scout missions, which is a series based on smaller satellites, lasting three years of development work and with a relatively limited budget of roughly 30 million for industrial contracts. We see the Scout missions as something very important for our space science work. The scientific community is evaluating them and these are the ones selected and endorsed by them.”

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Researchers from Fraunhofer and TeleOrbit delivered the project final presentation at a recent ESA-hosted event. Johannes Oeffner of Fraunhofer’s Center for Maritime Logistics and Services said “We wanted to look at different categories of biosensors and investigate the integration potential for PNT. The project brought together knowledge and expertise from a variety of scientific fields, looking at a range of different potential use cases and applications.”

Biosensors typically comprise a biological element, detecting specific biochemical reactions mediated by enzymes, immunosystems, tissues, organelles or whole cells, to detect chemical compounds. These elements are then coupled with a physical sensor or transducer that converts the biological-chemical signal into an electrical or optical signal. Biosensors are widely used in a number of applications, but are mostly seen in the healthcare field, in the monitoring and testing of medical events, in medical diagnosis. They are also used in environmental monitoring, for quality control in the pharmaceuticals and process industries, and in forensics.

Putting it together

The BIO.PNT first undertook an extensive analysis of different categories of biosensors, focusing on their potential for PNT integration. Field-effect transistor based biosensors for environmental monitoring were found to be very good candidates for combination with PNT. From there, the project developed the BIO.PNT sensor for the detection of pesticides within freshwater.

The selected bioreceptor is an organophosphate pesticide-cleaving enzyme combined with a transducer. The transducer comprises a modified field-effect transistor (FET) with amperometry, voltammetry or electrochemical impedance spectrometry (EIS).

System architecture is straightforward. One or more underwater sensor boxes contain physical biosensors for calibration and reference measurements, with pre-processing and signal processing via a microcontroller or analog front end specifically developed for the purpose. On the water’s surface, a communication box, powered by a solar panel, contains a low-power microcontroller serving as the primary control unit, and a GNSS/PNT module, with data storage handled via microSD card, and a communication module to send data to the user.

In summation, Oeffner said, “The BIO.PNT solution allows users to continuously detect organophosphate contamination in fresh water without sample preparation, in combination with PNT parameters that can be assigned to each measured value. This data would allow for environmental monitoring assessing water quality in natural ecosystems, lakes, and rivers, to locate, understand and mitigate the impact of human activities.

BIO.PNT was funded under ESA’s NAVISP program, supporting technology innovation in the European PNT industry.

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This will help The HALO Trust further its demining operations across the world. Building on the impact of the ongoing collaboration, Trimble’s latest donation will support the expansion and productivity of The HALO Trust’s mine clearance teams. The Catalyst GNSS system provides The HALO Trust with a solution for deploying precise mapping capabilities to large field teams across broad geographic areas. More field teams can now be equipped with the necessary tools to safely and efficiently clear landmines, thereby accelerating the pace of landmine clearance globally.

Since receiving Trimble’s product donations and the Trimble Foundation Fund directed grant, The HALO Trust has made remarkable progress in landmine and unexploded ordnance (UXO) clearance. From January to September 2024 alone, The HALO Trust cleared 802 minefields and battlefields, covering a total area of 10,400 acres across 12 war-torn countries. During this period, 31,209 landmines and other Explosive Remnants of War (ERW) were safely destroyed — all accurately mapped using the Trimble Catalyst GNSS system.

The HALO Trust’s use of Trimble technology has not only enhanced operational efficiency but also provided critical data for safe land reclamation and development. The accuracy and reliability of Trimble’s technology have been pivotal in ensuring the safety and success of demining operations in regions severely impacted by conflict, such as Ukraine, Angola and Sri Lanka.

“We are incredibly grateful for Trimble’s continued support,” said James Cowan, chief executive of The HALO Trust. “Trimble Catalyst and DA2 GNSS receivers have transformed our ability to map and clear minefields accurately. This new donation will enable us to expand our teams and reach even more affected communities, making a tangible difference in their lives.”

“The HALO Trust is making the world a better place,” said Emily Saunoi-Sandgren, director of environmental, social and governance (ESG) at Trimble and chair of the Trimble Foundation Fund. “Their dedication to humanitarian efforts aligns perfectly with Trimble’s mission of transforming the way the world works. By providing advanced technology solutions, we are enabling The HALO Trust to carry out their life-saving work more effectively.”

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