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 

(1) Zakharenkova, I., Astafyeva, E., & Cherniak, I. (2016). GPS and in situ Swarm observations of the equatorial plasma density irregularities in the topside ionosphere. Earth, Planets and Space, 68, 1–11. https://doi.org/10.1186/s40623-016-0490-5

(2) Alfonsi, L., Cesaroni, C., Spogli, L., Regi, M., Paul, A., Ray, S., & others. (2021). Ionospheric disturbances over the Indian sector during 8 September 2017 geomagnetic storm: Plasma structuring and propagation. Space Weather, 19, e2020SW002607. https://doi.org/10.1029/2020SW002607

(3) Heki, K. (2006). Explosion energy of the 2004 eruption of the Asama volcano, central Japan, inferred from ionospheric disturbances. Geophysical Research Letters, 33, L14303. https://doi.org/10.1029/2006GL026249

(4) Heki, K. (2011). Ionospheric electron enhancement preceding the 2011 Tohoku-Oki earthquake. Geophysical Research Letters, 38, L17312. https://doi.org/10.1029/2011GL047908

(5) Park, J., Von Frese, R. R. B., Grejner-Brzezinska, D. A., Morton, Y., & Gaya-Pique, L. R. (2011). Ionospheric detection of the 25 May 2009 North Korean underground nuclear test. Geophysical Research Letters, 38, L22802. https://doi.org/10.1029/2011GL049430

(6) Luhrmann, F., Park, J., Wong, W. K., & others. (2025). Detection of ionospheric disturbances with a sparse GNSS network in simulated near-real time Mw 7.8 and Mw 7.5 Kahramanmaraş earthquake sequence. GPS Solutions, 29, 54. https://doi.org/10.1007/s10291-024-01808-2

(7) Luhrmann, F., Park, J., & Wong, W.-K. (2026). Ionospheric anomaly detection and source geolocation over open ocean with GNSS remote sensing. Journal of Geophysical Research: Space Physics, 131, e2025JA034460. https://doi.org/10.1029/2025JA034460

(8) Tahami, H., & Park, J. (2020). Spatial-temporal characterization of hurricane path using GNSS-derived precipitable water vapor: Case study of Hurricane Matthew in 2016. Geoinformatica: An International Journal, 7(1).

(9) Kang, I., & Park, J. (2021). On the use of GNSS-derived PWV for predicting the path of typhoon: Case studies for Soulik and Kongrey in 2018. Journal of Surveying Engineering, 147(4). https://doi.org/10.1061/(ASCE)SU.1943-5428.0000369

(10) Martin-Neira, M. (1993). A passive reflectometry and interferometry system (PARIS): Application to ocean altimetry. ESA Journal, 17, 331–355.

(11) Löfgren, J. S., Haas, R., Scherneck, H.-G., & Bos, M. S. (2011). Three months of local sea level derived from reflected GNSS signals. Radio Science, 46, RS0C05. https://doi.org/10.1029/2011RS004693

(12) Larson, K. M., & others. (2012). Coastal sea level measurements using a single geodetic GPS receiver. Advances in Space Research, 51(8), 1301–1310. https://doi.org/10.1016/j.asr.2012.04.017

(13) Benton, C. J., & Mitchell, C. N. (2011). Isolating the multipath component in GNSS signal-to-noise data and locating reflecting objects. Radio Science, 46, RS6002. https://doi.org/10.1029/2011RS004767

(14) Williams, S. D. P., & Nievinski, F. G. (2017). Tropospheric delays in ground-based GNSS multipath reflectometry—Experimental evidence from coastal sites. Journal of Geophysical Research: Solid Earth, 122, 2310–2327. https://doi.org/10.1002/2016JB013612

(15) Kim, S.-K., & Park, J. (2019). Monitoring sea level change in the Arctic using GNSS-reflectometry. In Proceedings of the 2019 International Technical Meeting of The Institute of Navigation (ION ITM 2019), January 28–31, 2019 (pp. 665–675). https://doi.org/10.33012/2019.16717

(16) Kim, S. -K., Lee, E., Park, J., & Shin, S. (2021). Feasibility Analysis of GNSS-Reflectometry for Monitoring Coastal Hazards. Remote Sensing.2021, 13, 976. https://doi.org/10.3390/rs13050976 

(17) Semmling, A. M., Beyerle, G., Stosius, R., Dick, G., Wickert, J., Fabra, F., Cardellach, E., Ribó, S., Rius, A., Helm, A., Yudanov, S. B., & d’Addio, S. (2011). Detection of Arctic Ocean tides using interferometric GNSS-R signals. Geophysical Research Letters, 38, L04103. https://doi.org/10.1029/2010GL046005

(18) Soulat, F., Caparrini, M., Germain, O., Lopez-Dekker, P., Taani, M., & Ruffini, G. (2004). Sea state monitoring using coastal GNSS-R. Geophysical Research Letters, 31, L21303. https://doi.org/10.1029/2004GL020680

(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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The AsteRx EB is aimed at industrial robots, port logistics, marine platforms, and scalable automation systems — markets where accuracy requirements are demanding but where the volume economics of a high-end OEM module may not be practical. The IP67-rated housing protects against weather and dust, and the compact enclosure is designed to reduce installation time and simplify integration.

On the positioning side, the receiver incorporates Septentrio’s GNSS+ algorithms for performance in environments that challenge standard GNSS — foliage, urban multipath, proximity to interference sources. In a dual-antenna configuration, it delivers sub-degree heading alongside RTK-level positioning, covering applications that require both location and orientation. The AIM+ anti-jamming and anti-spoofing technology is built in, addressing the growing priority of interference resilience in industrial and autonomous systems.

“AsteRx EB is an ideal boxed receiver for customers who need reliable, resilient, and highly accurate positioning in a compact form factor and at a price point that makes rapid scale-up possible,” said Danilo Sabbatini, Product Manager at Septentrio.

The AsteRx EB slots into Septentrio’s enclosed receiver lineup between the mosaic-go evaluation platform and the AsteRx RB3, which is positioned for applications requiring the highest level of mechanical and environmental protection. Septentrio notes the EB can also serve as an evaluation platform for integrators assessing its positioning technology before committing to a production architecture.

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The system addresses a persistent problem in civil engineering, obtaining timely and integrated information on ground and structural movement from multiple sensor types in challenging environments.

“GEOvis is a monitoring platform for visualization,” said Amar Camo, Project Manager at Amberg Infra 7D, speaking to Inside GNSS at InterGEO 2025 in Frankfurt. “You can use it to visualize your monitoring data, but we can also collect data ourselves or host clients that have their own total stations or any sensors.”

The platform centralizes data from tunnels, railways, bridges, and urban construction sites, supporting real-time analysis and automated alerts. In tunneling and excavation, it tracks ground deformation and settlement. For rail projects, GEOvis provides millimeter-level precision to detect movement along tracks, while in dense urban environments it monitors potential displacement affecting nearby buildings or utilities.

At its core, GEOvis is a flexible web-based system that scales from manual campaigns to fully automated monitoring networks. It supports a broad sensor range, including total stations, inclinometers, crackmeters, vibration sensors, fiber-optic instruments, and GNSS receivers. The integration of GNSS data enhances the system’s capability to detect three-dimensional displacements over time, particularly in locations where optical instruments face line-of-sight limitations.

GNSS among a range of sensors

GNSS delivers absolute positioning and long-term stability, allowing GEOvis to import, process, and visualize satellite-derived data alongside terrestrial measurements. The platform’s architecture supports custom integration, a key differentiator according to Camo: “If the user comes and says, ‘Hey, we have this sensor’ we can integrate it into our software. It’s not a problem for us to adapt to what the user needs; our engineers are on it straight away.”

Founded in 2024, Amberg Infra 7D builds on more than 60 years of Amberg Group expertise in surveying, rail, and tunnel inspection. “The Amberg Group started as a normal survey company,” Camo said. “Then they created products that were very rail-oriented; Switzerland has a lot of tunnels and rail work, and they’re very careful about upkeep and inspection.”

Complementing GEOvis, the company offers Amberg Navigator, a field application that connects directly to total stations, laser scanners, or GNSS receivers for fast and guided measurements. “Navigator allows for very simple and easy measurements, say, in a tunnel,” Camo said. “You just point the instrument, it actually figures out where it is.”

Together, GEOvis and Navigator illustrate Amberg Infra 7D’s integrated approach to digital infrastructure monitoring, combining GNSS precision, real-time visualization, and adaptable field technology within a single ecosystem.

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Key features

The key features include:

• All-band RTK reception (L1/L2/L5/L6) across all satellite systems for unparalleled performance in urban canyons, dense canopies, and other difficult conditions.
• Integrated antenna system with diversity LTE, dual-band Wi-Fi, and Bluetooth for a clean GNSS signal and stable fix solution wherever you go.
• Emlid multi-band radio system up to 2W, interoperable with third-party gear for sending and receiving corrections. The radio supports both licensed and license-free operations at 450 MHz and 915 MHz. This allows users to adapt to project needs while staying compliant with regulatory requirements.
• Next-generation IMU tilt compensation with up to 5 times faster initialization.
• Durability-first design with a magnesium alloy body, IP68 protection, and user-replaceable bumpers to secure the receiver from the scariest falls.
• Made for iPhone certification for compatibility with iOS apps, including Esri ArcGIS apps.
• Innovative quick release mount for a survey pole, ensuring accuracy and speed, even when tilted. Сompatible with standard 5/8” thread.

Vision capabilities

Building on the RS4 platform, the Reach RS4 Pro adds groundbreaking vision capabilities. With dual, factory-calibrated full HD cameras, it delivers the new features:

• AR stakeout—an intuitive, augmented-reality guidance for fast and easy navigation to the points. Any geometry from the project can be projected in AR in the Emlid Flow app. 
• Measurement from images—a new solution to capture inaccessible points safely and quickly, be it on a facade or in the middle of a highway. The images taken directly from the receiver’s camera are processed in Emlid Flow to provide accurate coordinates.

With its camera capabilities, the Reach RS4 Pro goes a step further than traditional RTK. This next-generation receiver greatly speeds up stakeout and saves time for total station setup when measuring in hard-to-reach or hazardous areas.

Reach RX2 

The Reach RX2 is an ultra-portable RTK rover that is simple to use, with no settings to configure. Building on the strengths of the previous Reach RX, the new model is enhanced with greater performance in obstructed areas and new IMU tilt compensation.

The new features include:

• All-band RTK support (L1/L2/L5/L6) for greater reliability under canopy or in urban areas.
• Second-generation IMU tilt compensation for accurate, level-free measurements.
• The quick release mount for a fast and reliable setup in the field.

Pocket-sized and easy to deploy, the Reach RX2 is ideal for GIS, construction, and asset management in companies running multiple projects and teams. It integrates seamlessly with Esri ArcGIS apps for GIS data collection, and with the Pix4Dcatch app for mobile terrestrial scanning.

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This strategic partnership expands the market reach of both companies and directly addresses the growing demand for a complete mapping solution in the utility and critical infrastructure industries. By combining Bad Elf’s advanced GNSS receivers with ProStar’s patented precision mapping solution, utility owners, contractors, municipalities, and engineering firms are able to capture, record, and visualize the precise location of critical infrastructure at a low cost and with a complete solution.

“The demand for both high-accuracy GNSS receivers and mobile mapping solutions is rapidly accelerating across the critical infrastructure industry,” said Larry Fox, VP of Marketing and Business Development at Bad Elf. “This collaboration represents a major step in providing an affordable and complete mapping solution to a much wider range of customers on a global basis.”

Bad Elf is recognized for delivering accurate, compact, lightweight, and cost-effective GNSS solutions that are compatible with a broad range of third-party vendors. Together with PointMan, the bundled solution provides customers with a comprehensive, ready-to-deploy precision mapping solution that is designed to reduce costs, improve efficiency, and accelerate industry adoption.

Page Tucker, CEO and Founder of ProStar, stated, “Hardware providers are increasingly recognizing the importance of delivering a complete digital mapping solution to their customers. This trend has led to a growing number of new partnerships with leading equipment manufacturers that are bundling our mapping solutions. These collaborations are establishing a global distribution network that broadens our reach to tens of thousands of potential customers.”

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GEO-MEASURE: A Survey-Grade Rover

GEODNET has introduced GEO-MEASURE, a compact GNSS rover designed to deliver centimeter-level positioning at an accessible price point.

Key features from the announcement:

• Quad-frequency tracking across GPS, GLONASS, Galileo, and BeiDou systems, with support for “1,040 tracking channels” to enhance signal resilience in demanding environments. 
• The device is ruggedized and waterproof, intended for field use in professional settings. 
• It is paired with a companion mobile app (iOS/Android), allowing users to manage projects, view survey points on maps, take field notes, and export data in standard formats (CSV, KML, GPX, GeoJSON). 
• GEO-MEASURE comes with a preloaded RTK corrections service for the first year, aiming to streamline setup by eliminating the need for users to configure base stations or corrections manually. 
• GEODNET frames this release as part of its evolution from a corrections provider to a more comprehensive positioning product ecosystem. 

GEODNET & UTTO: Seamless Integration for Utility Mapping

On October 13, GEODNET announced that UTTO will become an official partner. This collaboration grants UTTO’s field devices and mapping tools direct access to GEODNET’s RTK corrections, removing the need for separate RTK configuration. 

Specifics from the partnership release:

• UTTO’s mapping solutions—including its vLocate Mapper®—will integrate GEODNET corrections out of the box, simplifying deployment for underground infrastructure and utility surveys. 
• UTTO users will benefit from GEODNET’s network of over 20,000 active stations globally, aiming for consistent high-precision coverage across regions. 
• A major stated advantage is reduced friction for field teams, as the corrections service becomes transparent to the end user; no manual RTK setup is required.
• The partnership emphasizes interoperability with leading GIS platforms (e.g. Esri ArcGIS) to support workflows in field asset mapping and underground utility verification. 

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The new solutions will be showcased at INTERGEO 2025, the trade fair for geodesy, geoinformation and land management, held in Frankfurt, Germany, October 7-9.

INTERGEO marks the first showing of new products following the announcement of the new dedicated business structure focused on delivering high-precision technology, software, and services for geomatics. Neil Vancans, head of the new Geomatics Sales Unit, said, “We are entering a new chapter focused on helping professionals work smarter by streamlining workflows, boosting collaboration, and simplifying digital transformation.”

Topcon will launch a new scanning portfolio, accompanied by a software platform designed to be a long-term companion to customers. “The new scanning portfolio and software platform will enable us to provide the benefits of AI to the common workplace for a multitude of sensors. The principle is flexibility. By integrating sensors across price points and accuracy levels into a common platform, Topcon aims to provide customers with the right tool for the right job,” Vancans said.

“Customers are looking for streamlined and effective workflows. Topcon is addressing this need by prioritizing workflow simplicity, open integrations, and compatibility with mixed fleets. We are designing an ecosystem that will provide value for the customer.”

Foundational tools for Topcon’s connected workflow are Collage and ClearEdge software, which allow for data, feature extraction, and the seamless delivery of results into GIS, BIM, or CAD environments. Topcon Collage, available in Site, Office, and Web versions, serves as a central data hub, enhancing data sharing among all project stakeholders in both the field and office.  Topcon is also introducing the Collage Cloud Connector for improved connectivity with Autodesk platforms and ClearEdge solutions such as EdgeWise or Verity. 

Featured hardware and software include:

Collage Cloud Connector: New Windows app designed to simplify and automate the process of downloading and synchronizing project data from Collage Web to a local machine for use with Autodesk, ClearEdge3D software, or Collage Office.

CR-H1 handheld scanner: The CR-H1 handheld mapping solution utilizes iPhone Pro devices with integrated LiDAR that collects georeferenced images and employs photogrammetry to create detailed, full-color 3D point clouds. The iPhone connects to the Topcon HiPer CR receiver, enabling centimeter accuracy with RTK corrections from the Topnet Live GNSS corrections service. The receiver and iPhone are both mounted on a specialized handle, so users can easily capture point clouds while walking throughout the job site. The devices needed can quite simply be held in one hand for maximum mobility in the field.

CR-M1 scanner and new Onami software: The CR-M1 is an indoor/outdoor mapping system that can be utilized on a backpack or on a survey pole. The CR-M1 is ideal for urban and construction mapping, multi-floor buildings, real estate, underground mapping, mines and tunnels, stockpiles, and forestry environments. Publishing and sharing the CR-M1 data with Collage Web allows the user fast, web‑based visualization and manipulation of their 3D point clouds and meshes.

CR-P1 multi-functional 3D terrestrial laser scanner: The CR-P1 provides real-time, georeferenced point cloud generation on-site, equipping the user with actionable data that can be used for various applications. Enhancements to the Topcon Collage Site software solution provide new mass data workflow capabilities designed to allow for the real-time acquisition and processing of data more accurately, efficiently, and quickly to provide the user with greater productivity.

CR-S2 handheld scanner with Magnet Flow and Bridge software: The CR-S2 is a handheld scanning system that uses multiple localization fusion-SLAM to perform mapping projects in challenging environments. It is designed to work in construction and infrastructure applications as well as open, featureless environments such as beaches, coastlines, farmland, and airports. In open environments, the RTK connection to the Topnet Live network ensures position quality. Publishing and sharing the CR-S2 data into Collage Web allows the user fast, web-based 3D point cloud and mesh visualization with easy-to-use measurement and annotation tools.

LN-1000i Layout Navigator with Topcon Digital Layout software: The LN-1000i is an addition to the Layout Navigator series that includes new features, including reflectorless measurement capabilities, green laser beam pointer and an integrated camera for live view, camera control and enhanced prism lock. When combined with the new Topcon Digital Layout 2.0 software, this instrument is designed to set new standards in building construction.

Expanded Hybrid Positioning capabilities with Topcon software updates for Field – Office – Tools – Enterprise: Allows for better cooperation switching between GT robotic total stations and the HiPer XR GNSS receiver as an optical and GNSS hybrid solution. The new version 10 Topcon Field software enables the user to use the tilt of the IMU when measuring to the prism in Hybrid Positioning mode.

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At INTERGEO 2025, Vexcel Imaging unveiled two major additions to its UltraCam portfolio—the UltraCam Dragon 4.2 and the UltraCam Merlin 5.0—alongside upgrades to its UltraNav and UltraMap software suites. Together they illustrate how precise navigation, rather than optics alone, now defines the frontier of airborne mapping.

Hybrid Imaging Built on a GNSS Core

The UltraCam Dragon 4.2 introduces a fully hybrid sensor concept that merges photogrammetry and LiDAR within a single housing. The system integrates a RIEGL 2.4 MHz airborne LiDAR module with Vexcel’s large-format imaging array, synchronized through a tightly coupled GNSS-INS navigation unit derived from the company’s UltraNav architecture. The coupling allows direct, centimeter-level co-registration between imagery and point-cloud data during flight, reducing the need for post-mission alignment and minimizing cumulative georeferencing error across long corridors.

For large-area mapping and national orthophoto programs, Vexcel also presented the UltraCam Merlin 5.0, its next-generation high-altitude camera. The Merlin features enhanced exposure synchronization linked directly to UltraNav’s multi-constellation receiver set, which now supports GPS, Galileo, BeiDou, and GLONASS in both real-time PPP and post-processed PPK modes. The integration improves absolute positioning accuracy and time-tag precision, ensuring that each image frame is tied to a unified coordinate framework.

Software as Navigation Infrastructure

Complementing the new hardware, UltraNav 3.2 introduces refined inertial-GNSS blending algorithms and a common metadata format that propagates accuracy estimates from the navigation solution through to image and LiDAR products. The updated UltraMap 7.0 platform reads this metadata directly, harmonizing mixed-sensor datasets inside a single processing pipeline. Users can monitor GNSS quality indicators—satellite geometry, multipath statistics, and estimated positional uncertainty—within the same interface used for radiometric and geometric calibration.

Toward Navigation-Defined Imaging

Vexcel positions these releases as part of a long-term shift toward navigation-defined imaging systems, where GNSS and inertial integration form the foundation of every sensor operation. By embedding the positioning engine inside the camera architecture and linking it to downstream processing, the company aims to deliver sub-decimeter accuracy without dense ground control, shortening turnaround times for both commercial and governmental mapping missions.

At INTERGEO 2025, that message resonated with a broader industry theme: the convergence of imaging, LiDAR, and navigation into unified aerial data platforms. For the GNSS community, Vexcel’s latest systems exemplify how precision positioning continues to migrate from a supporting role to the central organizing element of modern geospatial intelligence.

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The solution combines Trimble’s cutting-edge, industry-leading technologies: Trimble ProPoint® positioning engine, post-processed Trimble CenterPoint® RTX (POSPac PP-RTX) and Applanix IN-Fusion+ multi-sensor aided inertial engine. 

This combination offers geospatial professionals the ability to deliver unparalleled accuracy and efficiency in the crewed and uncrewed airborne (UAV), land and marine mobile mapping and surveying industries.

POSPac Complete will be showcased at INTERGEO 2025, the world’s leading trade fair for geodesy, geoinformation and land management, where Trimble is a platinum sponsor.

A redesigned software solution embedded with POSPac PP-RTX, Applanix POSPac Complete is available exclusively as an all-in-one term license that bundles essential GNSS augmentation options — single base, Applanix SmartBase post-processed VRS and POSPac PP-RTX service — into a single, transparent annual fee. This eliminates hidden costs, simplifies budgeting and ensures access to software updates, while Trimble RTX removes the need for base stations and provides global coverage.

Because Trimble RTX is embedded into the software, users around the world can attain seamless and efficient workflows with centimeter-level accuracy, even in remote or inaccessible areas, greatly increasing productivity in their mapping process. Additional time savings are gained with the elimination of the time-consuming and challenging task of setting up and managing base stations that may be in different local datums or epochs.

“The new POSPac Complete is more than just a software update; it’s a paradigm shift in how geospatial professionals achieve high-accuracy results because of Trimble’s unique RTX factor,” said Nico Jaeger, product manager at Trimble. “By integrating the power of PP-RTX directly into the software, we’ve eliminated the logistical headaches of base stations and streamlined the entire workflow, making mobile mapping more accessible than ever before. Altogether, it helps new and experienced users to produce survey-grade results with unprecedented speed and simplicity, truly representing the next generation of geospatial processing software.”

Additional Features in the New POSPac Complete Include:

• Modernized user interface: A new look and feel with a background map and a streamlined project wizard for easier and more efficient workflows, and a better user experience.
• Trimble IonoGuard™: Trimble’s latest technology that detects and mitigates the effects of ionospheric scintillation, which is especially important during the solar activity peaks, supported in single base and PP-RTX processing modes.
• Optional add-on features available for purchase:
  • Camera QC tools: The robust successor to CalQC, providing rapid IMU to camera boresight calibration for single-head and multi-head (oblique) camera constellations with minimal user interaction.
  • LiDAR QC tools: Trimble’s leading software application for IMU to LiDAR boresight calibration and trajectory adjustment using SLAM techniques is now enhanced with support for automatic ground control point (GCP) detection, the RIEGL Lidar native file format and reduced RAM requirements for faster processing.

The POSPac Complete solution will be available in November 2025 through the Trimble sales channels. For more information or to request a demo, please visit https://applanix.trimble.com/en/software/applanix-pospac-complete.

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Render Networks, a leader in digitalizing network construction management, announced today a new integration to Trimble® Mobile Manager (TMM), bringing Trimble’s high-precision GNSS capabilities to broadband and utility network deployments. This integration enables centimeter-level accuracy at the point of construction, minimizing delays and ensuring that as-built records are complete and verifiable from day one.

Telecom and utility builds demand precise asset location for compliance, maintenance, and future locates — from fiber and conduit to drops, handholes, splice points, poles, or valves. Traditional methods often require rework, field revisits, and manual reconciliation with GIS, slowing progress and straining budgets. The integration enables Render users to consume high-precision positions from Trimble receivers, including the Trimble DA2 with Trimble Catalyst and the Trimble R2, directly within Render Networks’ mobile app.

“With the increased fiber infrastructure demand due to the BEAD program award and timelines, capturing sub-centimeter location data the first time is essential,” said Craig Schellenbach, GIS Solutions Architect, at ADB Companies. “The ability to combine Trimble’s high accuracy solutions with Render’s automated construction workflows delivers survey-grade as-builts and close out documentation for our projects in real time, the first time.”

With Trimble high-precision GNSS now integrated, Render Networks’ customers can deliver fast, accurate, and verifiable as-builts as part of their normal workflows. This eliminates the need for site revisits, reduces rework, and streamlines project acceptance.

“Our customers are building critical infrastructure at massive scale, and high accuracy data is non-negotiable,” said Rob Laudati, Chief Product and Partner Officer, at Render Networks. “With this new integration, we’re giving crews the ability to capture as-builts with location accuracy in real time, accelerating closeout and ensuring data quality that supports compliance, operations, and asset management for decades to come.”

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