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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Researchers used a specially equipped Safire ATR-42 aircraft to record GNSS signals over a variety of sites in France, including agricultural areas, forests and selected bodies of water. Onboard the aircraft were three GNSS recorders, based on the Stella Record and Playback (Stella RP) solution from M3 Systems combined with CESBIO’s Global Navigation Satellite System Reflectometry Instrument (GLORI).

Hardware was selected and configured to achieve the highest high-quality recording. The setup included two antennas provided by CNES and CESBIO, one pointing towards zenith and the other towards nadir. GNSS signals were recorded simultaneously on four channels: one channel for direct, i.e. zenith, L1/E1 signals with RHCP polarization, a second channel for direct L5/E5a signals with RHCP polarization, a third channel for reflected, i.e. nadir, L5/E5a signals with RHCP polarization, and a fourth channel for reflected L5/E5a signals with LHCP polarization. Partners employed 8-bit quantization and an OCXO clock for maximum precision.

Onboard the ATR-42 during data acquisition were CESBIO’s Pascal Fanise, Carlos Davis of M3 Systems and Robin Quinart from CNES. Coincident ground-truth tests were also carried out, including determination of in-situ soil moisture levels, leaf area indices and other measures, to confirm airborne reflectometry measurements and the results of data post-processing.

Environmental applications

In addition to providing valuable insights into forest biomass and soil moisture, the study has delivered data collected over bodies of water and at sea that can potentially serve altimetric applications. Altimetric information, including wave height, can be obtained by analyzing time difference and phase difference between direct and reflected GNSS signals, a technique that has been employed successfully in a number of other environmental studies.

Over the past decade, CNES has carried out several GNSS reflectometry-based projects, highlighting the growing use of GNSS in scientific applications and particularly in environmental studies. CNES has also collaborated with M3 Systems on numerous projects since 2016. Notably, M3 Systems has developed a GNSS software receiver with specific reflectometry capabilities for CNES. Closing the circle, CESBIO has had occasion to deploy said M3 Systems GNSS software receiver through its collaboration with CNES.

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Innovative Monitoring Method

When Greenland’s ice sheet melts, the underlying bedrock rises due to reduced pressure. This elevation change, detectable by GPS, translates into precise measurements of ice loss. Valentina Barletta, a senior researcher at DTU Space, explained the significance: “This is the first time we can measure the entire mass loss of the ice sheet day by day.”

The new method, detailed in Geophysical Research Letters, allows for daily monitoring, a significant improvement over previous methods that provided monthly or annual estimates. Greenland loses about 5 cubic kilometers of ice per week, equivalent to draining Denmark’s largest lake, Arresø, 40 times weekly.

Practical Applications and Benefits

The new GPS-based method not only advances climate research but also offers practical benefits, such as flood warnings for Greenland residents. By monitoring daily changes in ice mass, local populations can be warned of potential flooding due to sudden meltwater release, as experienced in Kangerlussuaq in 2012.

The system uses data from the Danish state’s GNET, operated by the Danish Geodata Agency in collaboration with DTU. The GNSS technology, which includes GPS and Galileo, detects sub-millimeter changes in bedrock movement.

Malte Nordmann Winther-Dahl, project manager for GNET, emphasized the importance of maintaining these measurement stations: “We are pleased that data from the GNET stations is so widely used and gives us new opportunities to accurately monitor climate change in Greenland.”

Implications for Climate Science

This method complements existing techniques such as NASA’s GRACE satellites, altimetry satellites, and ice movement measurements, providing a more comprehensive understanding of ice mass loss. The study and the new method were developed in collaboration with DTU Space and DTU Compute, leveraging their expertise and computational power.

The enhanced monitoring capability will aid the UN Intergovernmental Panel on Climate Change (IPCC) in making better estimates for future ice sheet melting and its contribution to global sea level rise, thereby improving climate change predictions and response strategies.

Read more about the research via DTU.

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Dr. Michael Steckler, Lamont Research Professor, Marine and Polar Geophysics, Lamont-Doherty Earth Observatory (LDEO) writes:

“The GNSS will measure the subsidence of the land below the buildings that they are mounted on. The RSET-MH [Rod Service Elevation-Marker Horizon] will measure the subsidence caused by sediment compaction above the base of the rod at 80’ below the ground. It does this by carefully measuring elevation with the RSET and the sediment accumulation with the MH. The difference is the shallow subsidence. Finally, for the first time we are adding augering. The auger will be driven into the ground to take sediment samples…These results will be important for the sustainability of the Ganges-Brahmaputra Delta and Bangladesh. Sea level rise is augmented by land subsidence, but countered by sedimentation building up the land. Understanding how these three factor balance is critical to this low-lying land.”

The 4-part field report provides observations from travel through Bangladesh, and details the Columbia team’s collaboration with local research partners. Read the full field report on this important work via Columbia Climate School Earth Notes.

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DR. JIHYE PARK, FIONA LUHRMANN, DR. WENG-KEEN WONG, OREGON STATE UNIVERSITY

Ionospheric responses to various geophysical events have been studied for decades, as they can play a key role in triggering events. When geophysical events such as earthquakes, tsunamis, volcanic eruptions, magnetic storms, tropical storms, and large-scale explosions occur, gravity waves, gravity acoustic waves, or acoustic waves are generated and propagated along the lower and upper atmosphere.

Researchers have used various sensors to detect the signatures of these waves, which are traveling ionospheric disturbances (TIDs) [1-5]. Early and accurate detection of TIDs can provide valuable information on event origin and velocity of the produced waves. However, detecting TIDs is challenging because of the background ionospheric behaviors and the numerous sources that can trigger disturbances in the ionosphere. While various sensors have been used to monitor TIDs, the advent of GNSS has facilitated research development. As the ionosphere is a dispersive medium for GNSS signals, dual or multiple frequency GNSS signals can extract the total electron content (TEC) on a single line of sight. TIDs can be detected by analyzing TEC time series. 

Although a number of researchers have investigated ionospheric responses to different natural and artificial geophysical events using GNSS and other ionospheric observing sensors, some critical problems remain. One fundamental question about ionospheric observations is how signatures detected from a particular source event can be isolated from the ionosphere’s background noise. Because of the ionosphere’s dynamic nature, it is crucial to filter out the noise and other effects through sufficient data processing. Another fundamental question is if the detected TID can be properly discriminated between many source events. While a few studies [6,7] showed different properties of TIDs between a few selected events, comprehensive analyses for many events are limited.

Recently, Artificial Intelligence (AI) algorithms have proven beneficial because of their capacity for massive data processing, reducing the potential for human errors. With the growing number of GNSS infrastructure including dense GNSS continuous operating reference stations (CORSs) on the ground and the enhanced space segment through multi frequency multi constellations, AI techniques become feasible for many GNSS research topics that includes ionosphere monitoring using GNSS, namely GNSS remote sensing. State-of-the-art AI applications to GNSS remote sensing have leveraged various AI techniques, but they mostly focus on TEC prediction [8], forecasting TEC disturbances [9] and detecting scintillation [10]. There has been limited work in induced TID detection that employs deep learning-
based anomaly detection methods [11]. 

Moreover, previous studies have detected TIDs induced by various historical events. Event induced waves detected in the ionosphere include the 2011 Tohoku, Japan earthquake [12] and following tsunami [13], the 2022 Tonga volcanic eruption [14] and non-natural hazards such as nuclear events [5]. Connecting a TID to a natural hazard event commonly relies on previous knowledge of the event and a human-lead search of station data to discover a potential detected TID induced by the event. This work aims to reverse these steps, detecting a TID from TEC time series data, potentially with the ability to precede the confirmation of a natural hazard event. In this study, we applied Long Short-Term Memory (LSTM) networks, a deep learning technique to detect ionospheric anomalies that can be considered as TIDs, and used different algorithms to relate them with their source event. 

Approach: Deep Learning Based TID Detection

As a proof of concept for the automated TID detection method, we performed an experimental study using a test site in Alaska and processed a year-long dataset from 10 ground-based GNSS sites. Alaska is located in high latitudes where ionospheric anomalies may frequently appear due to not only natural hazards such as earthquakes but also other effects caused by geomagnetic storms or phase scintillations.

LSTM Based Anomaly Detection

Deep learning based anomaly detection uses deep learning neural networks to identify patterns in data and detect outliers. The basic idea is to train a neural network on a large dataset of normal data, and then use the trained model to identify any data points that deviate significantly from the learned pattern under normal conditions. This can be done using various deep learning neural network architectures. Recurrent neural networks (RNNs) are one type of neural network architecture that can model sequential data [15] for anomaly detection. RNNs are based on a feedforward network and include a mechanism that can retain past information to forecast future values [16]. LSTM is a type of RNN [17] capable of learning long term dependencies and are robust against the vanishing and exploding gradient problem of conventional RNNs [18]. LSTMs selectively remember or forget information over long time intervals, which makes them useful for tasks that involve sequential data. Recent studies applied LSTM methods to predict the ionospheric F2 parameter [8], disturbances during geomagnetic storm periods [19] and scintillations [20]. 

For TEC anomaly detection in this project, we used the 15 second sampling rate GNSS observations to obtain TEC values with 15 second sampling intervals. To avoid the impact of satellite geometry changes, solar activities and biases, the trend of TEC time series is removed and the remaining detrended TEC (dTEC) is used as input features. The input sequence of 15-minute-long TEC data—which is equivalent to 60 time steps—is used to predict the next value of TEC, or the next 15 second sample point. The input sequence was fed into a two-layer LSTM network with 256 neurons in each hidden layer, followed by two fully connected layers each with 128 neurons. The LSTM was trained for 25 epochs and used a scheduled learning rate from 1e-3 to 1e-6, reducing at epochs 10, 15 and 20. The batch size was set to 64. The loss function used to train the model was mean squared error (MSE). The final architecture was obtained by reaching the lowest MSE scores on the training data.

Error Processing and Analysis

An error threshold is set by combining a rolling MSE with a multiple of the MSE standard deviation. This dynamic threshold was chosen to aid in managing day-to-day variations in dTEC measures. The number of errors within a rolling time window reflects the extent of the possible disturbance. Therefore, as MSEs are processed, we use a rolling MSE error count over 10-minute windows (or 40-time steps). If we observe more than one error within the rolling window, we then pull the data starting 10 minutes prior to when the error count exceeded one and end when the error count drops back below or equal to one. This slice of the data is then looped through each station-station pair (for 10 stations, this is 45 unique pairs) to measure phase synchrony between each pair, accounting for lag. If phase synchrony exceeds 0.9, a high synchrony level, among stations during times of observed errors then we label the resulting date time slice an ionospheric anomaly, with the time of the first observed anomaly as the time of when the error count exceeded one.

Phase synchrony is calculated between two station pairs with Equation 1 where the angle of the dTEC value is the Hilbert Transform of the dTEC. In Equation 1, φ_syn is the phase synchrony between two stations, φ₁ is the dTEC phase angle of the first station and φ₂ is the dTEC phase angle of the second station.

Experiments

On July 29, 2021, there was a Mw 8.2 earthquake reported south of Perryville, Alaska. We applied the proposed approach and attempted to verify the method by checking if the algorithm could detect the TID associated with the earthquake.

GNSS observations were obtained from UNAVCO for 10 stations: AB02, AV10, AV12, AV06, AV26, AC42, AB06, AC25, AC21, and AB13, between latitudes of 52°-57°N and longitudes of 158°-169°W. Corresponding broadcast navigation files were obtained from the National Oceanic and Atmospheric Administration (NOAA) Continuously Operating Reference Station (CORS) Network (NCN) data archive. These stations are located along the Alaska Peninsula and Aleutian Islands. They were chosen for their nearly complete observations each day in 2021 (Figure 2).

Slant TEC (sTEC) measurements were calculated with the geometry-free linear combination formula using dual-frequency carrier phase signals [21] between these stations and satellite GPS 04, part of the GPS Block III iteration. The line of sight between these stations and GPS 04 crosses the Gulf of Alaska and Pacific Ocean, south of the stations, a location with high levels of tectonic activity. An elevation angle cut off was set to 20 to remove edge effects. To improve detection results, sTEC data was filtered using the fourth-order Butterworth high pass filter to remove frequencies below 1 MHz. The resulting data is now labeled as dTEC.

The final dTEC data set, after removing NaN values, resulted in a time series length of 407,366 for the entire year of 2021, for each station-satellite pair (Figure 3). The data set was divided into training, validation and test data sets. Training data included January through the end of May, validation included June, and test data included July through the end of December. All data was normalized for LSTM training and testing purposes.

Results

The LSTM prediction results capture the temporal dynamic of the dTEC values across the 10 stations (Figure 4). Therefore, using this model for predicting error anomaly detection is valid. A total of 141 errors were detected across 43 days using the error thresholding technique (Figure 5). Five date times exceeding a rolling error count of one (Figure 6) with station pairs exhibiting high phase synchrony during flagged errors were detected. These five datetimes are listed in Table 1.

The ionospheric anomaly detected starting July 29, 2021, at 06:26:15 (UTC) exhibited errors above threshold across four nearby stations, at the southwestern most location of stations in this study. The same four stations plus two additional ones also exhibited high phase synchrony during the same time as their errors. This anomaly datetime is roughly 10 minutes after the Chignik, Alaska Peninsula earthquake, which occurred on July 29, 2021, at 06:15:49 (UTC) according to the U.S. Geological Survey National Earthquake Information Center (USGS-NEIC). This magnitude 8.2 earthquake was located at 55.364°N 157.888°W, at a depth of 35.0 km. 

The ionospheric anomaly detected at datetime starting on August 28, 2021, at 00:24:00 (UTC) exhibited errors above threshold at four stations with non-overlapping high phase synchrony between two station pairs. As of the time of publication, this anomaly source has not been identified.

Summary and Discussion

Detecting ionospheric disturbances is an important undertaking in geolocation, particularly with respect to natural hazards like earthquakes and tsunamis, as well as anthropogenic hazards such as nuclear and ballistic weapons tests. These ionospheric disturbances are often reflected in TEC after proper signal processing such as detrending (dTEC in our study), but detecting TID as they occur is an open area of interest in geolocation research. In this article, we implemented a deep learning model using the LSTM architecture to predict dTEC for 2021 from data across 10 stations in Alaska. We then used the error threshold, error count and phase synchrony methods to identify timesteps in the dTEC data where the measurements are potentially natural hazard induced TID. Because we expect the LSTM to accurately predict “normal” dTEC behavior, large disparities between the prediction and the measured dTEC should theoretically indicate the presence of an anomaly related to an event such as an earthquake. 

We compared the LSTM dTEC prediction error with the prediction error generated by a naive forecaster (i.e., a predictor that predicts the value at time t+1 to be equal to the value at time t). The LSTM outperformed the baseline by six times with respect to mean squared error on the test data. This indicates a significant improvement over the naive model. Of the two ionospheric anomalies identified in 2021 with this method, one was the result of the magnitude 8.2 earthquake and the other unknown. Based on these results, we conclude the LSTM anomaly detection method is a viable option for further contributions to automate TID detection across long term observations in the North America region.

Using an ionospheric pierce point (IPP) of 250 km, based on Incoherent Scatter Radar (ISR) observation of ionosphere altitude at this time, we observe pierce points over the Gulf of Alaska, with station AB06 IPP directly over the earthquake epicenter (Figure 8). From Figure 8 we observe dTEC amplitude increase starting from the earthquake epicenter along a vector southwest, roughly parallel to the coastline, with amplitudes increasing from +/- 0.25 dTEC (TECU) to +/- 0.90 dTEC (TECU) along this path.

Plotting IPP distance to earthquake epicenter over time, and the time of first peak (Figure 9), we can use a weighted linear fit, weighted by absolute amplitude values, to determine the velocity of the disturbance. This results in an approximate 1 km/s velocity of what is likely an induced TID by the Chignik earthquake event.

An additional and important area of continued research is the ability to identify the cause of these detected ionospheric anomalies. We plan to continue working on developing a method that can classify the results of these detected ionospheric anomalies, predicting the potential source. 

References 

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Authors

Dr. Jihye Park is an associate professor of Geomatics in the School of Civil and Construction Engineering at Oregon State University. Dr. Park holds a Ph.D. in Geodetic Science and Surveying from The Ohio State University. Her research interests include GNSS positioning and navigation, GNSS remote sensing, GNSS meteorology, and GNSS-Reflectometry for monitoring the Earth’s environments, natural hazards, as well as artificial events.

Fiona Luhrmann is a research graduate assistant at the College of Engineering pursuing a Ph.D. of Geomatics Engineering and a minor in Artificial Intelligence at Oregon State University. Her research interests are machine learning applications in GNSS signal processing and atmospheric monitoring.

Dr. Weng-Keen Wong is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He received his Ph.D. (2004) and M.S. (2001) in Computer Science from Carnegie Mellon University, and his B.Sc. (1997) from the University of British Columbia. His research areas are in data mining and machine learning, with specific interests in anomaly detection, probabilistic graphical models, computational sustainability, and explainable artificial intelligence.

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The researchers report on the results of pairing GNSS data with seismic anisotropy data:

Key points

• For the first time, Global Navigation Satellite System (GNSS) deformation data has been collected and used simultaneously with seismic anisotropy data to determine the regional distribution characteristics of surface and upper crustal deformation. Application of this technique in the northeastern Tibetan Plateau and its adjacent areas is summarized.
• Differences between surface and upper crustal deformation are analyzed, revealing differences in regional deformation mechanisms.
• Combining GNSS data with seismic anisotropy data can reveal depth variation differences in regional crustal deformation.

– Huyu Li, Yuan Gao, Honglin Jin, Upper crustal deformation characteristics in the northeastern Tibetan Plateau and its adjacent areas revealed by GNSS and anisotropy data, Earthquake Science, 2023.

The GNSS data reveals surface deformations caused by seismic activity, the authors reporting that “GNSS velocity fields reflect the kinematic characteristics of tectonic activity, directly revealing a tectonic deformation pattern.”

Read the full text of the research article via ScienceDirect.

The post Weekend Read: GNSS Data Advancing Seismology Research on the Tibetan Plateau appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

This collaboration has introduced a cloud-based positioning solution to monitor the activities of Adélie penguins in one of the most inhospitable environments on Earth, Ross Island, Antarctica. The venture began when Point Blue, a prominent American wildlife conservation organization, reached out to CTT to devise a tracking mechanism that could study the movements of juvenile Adélie penguins. These birds are among the five penguin species inhabiting Antarctica and are considered crucial for environmental studies due to their sensitivity to ecological shifts caused by climate change and human activities, such as commercial fishing.

Given the challenges posed by the harsh Antarctic conditions, including the penguins’ small size, rapid movement, and extended underwater dives, the project required a tracking device that was not only lightweight and non-intrusive but also energy-efficient and cost-effective in terms of data transmission.

To meet these stringent requirements, u-blox provided its CloudLocate positioning service, enabling the development of CTT’s Penguin Iridium GPS tracker. This innovative solution is affixed to the penguins’ backs and leverages one of u-blox’s compact GNSS modules, known for its minimal energy consumption. The CloudLocate service processes the positioning calculation in the cloud rather than on the device, offering a tenfold energy saving and enabling prolonged monitoring periods without the need for frequent device replacements.

The tracker operates by sending a concise 50-byte message, which captures essential location data during the brief moments when a penguin surfaces for air. This efficiency is critical in minimizing the costs associated with satellite connectivity, which is often prohibitively expensive in such remote regions.

This partnership marks an advancement in wildlife conservation technology, allowing for non-invasive, continuous monitoring of Adélie penguins and providing vital data for assessing the impacts of environmental changes. CTT aims to extend this novel tracking solution to a wider range of wildlife research and conservation projects, demonstrating the potential for technological innovation to contribute significantly to ecological preservation efforts.

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DANIEL NEAMATI, SRIRAMYA BHAMIDIPATI, 
GRACE GAO, STANFORD UNIVERSITY

3D mapping aided GNSS localization provides state-of-the-art urban positioning by leveraging 3D building maps to account for reduced satellite visibility. Shadow matching is at the core of 3D mapping aided GNSS whereby the user matches the signal degradation at the receiver to the predictions from a 3D building map. Unfortunately, symmetries in building geometry yield multiple regions where the user could be located. With set-based techniques, we can fully account for these ambiguous regions and process the GNSS pseudorange information over each region individually to improve localization.

GNSS Shadows in Urban Environments

City dwellers and urban autonomous systems rely on the Global Navigation Satellite System (GNSS) to provide absolute location services. However, urban infrastructure often degrades standalone GNSS systems [1,2], thereby preventing reliable positioning, navigation and timing. Buildings block, diffract and reflect the line-of-sight (LOS) GNSS signals, thus inducing non-line-of-sight (NLOS) and multipath effects. 3D mapping-aided GNSS (3DMA GNSS) localization has gained traction over the past decade with the increasing availability of high-accuracy 3D city models. Shadow matching is a popular technique for 3DMA GNSS [3,4], among others, such as ray tracing [5,6] and machine learning-based GNSS [7]. Chiefly, the GNSS shadow refers to the areas where city infrastructure blocks direct LOS signals from a GNSS satellite. The user refines the location estimate by determining if the user is inside or outside the GNSS shadow, generally using signal features like signal-to-noise ratio. In this way, the user can turn NLOS and completely blocked signals into valuable information for localization. In past Inside GNSS articles, several authors elaborate on the critical role of shadow matching in 3DMA GNSS [8-11].

While shadow matching improves reliable urban positioning, particularly in the cross-street direction, it also suffers from challenges that restrict its performance. A discussion of the challenges in shadow matching is included in [12], with two two key challenges being a) along-street accuracy is often not reliable and b) multiple positions with large scores yield a multi-modal and ambiguous localization. With a denser urban scene, the location ambiguity often worsens.

We illustrate these challenges in Figure 1, where we extend the common two-dimensional depiction of shadow matching to a slightly larger scene with two streets and different buildings in the foreground and background. The task of shadow matching is to narrow the user location based on the GNSS shadows. For clarity, we only show the shadows of two satellites. The satellite’s shadow is the color-coded region from the building roofs to the ground where the user would receive a highly degraded (i.e., NLOS) GNSS signal or no signal. If the user is on the street outside and has received an NLOS signal or no signal from both satellites, the magenta segments are the only valid user position sets. With only two moderate-elevation satellites, we significantly narrow the user’s location. However, we have not narrowed the along-street dimension (i.e., foreground and background) and we have multiple disjoint valid user sets, so the localization is ambiguous.

We further illustrate these issues of ambiguous localization in Figure 2 as a top view of the scene. The azimuth distribution of satellites is often helpful to localization, especially in cities where building height is variable and buildings have gaps between structures. In Figure 2, the satellites are roughly 140 degrees separated in azimuth. From the top view, we can fully detail the valid user set as a 2D polygon (magenta). In this example scene, there are five disjoint sets for the user position sets that match the user being on the street outside and having received an NLOS signal or no signal from both satellites. We could further reduce the valid user set into smaller sets with additional satellites. However, we are often left with multiple disjoint regions that match the available GNSS shadow information, especially in the along street direction [13].

One possible strategy for improving shadow matching’s along-street accuracy and reducing multi-modal ambiguities in localization is to fuse shadow matching with GNSS pseudoranges. Several authors pioneered this work in improving the urban position accuracy via weighted integration of shadow matching position solutions with that of likelihood-based 3D-mapping-aided GNSS pseudorange ranging [14-16]. The different integration options are reviewed and analyzed in [16]. These methods were further developed into a multi-epoch grid filtering framework in [17,18], which demonstrated improved along-street and cross-street accuracy.

These works built upon prior shadow matching filtering work, such as [19], that combined shadow matching in particle filter and Kalman filter frameworks to resolve multiple modalities over time.

The Set-Based Positioning Paradigm

While past 3DMA GNSS techniques have been successful, they rely on formulating shadow matching in a grid-based manner, which may not be as suitable as a set-valued approach for some users. As illustrated in Figures 1 and 2, shadow matching can be geometrically posed in the following set-based terms: the user is either in the shadow (which is a 2D set or polygon) or outside the shadow (which is the complement set). Set-based formulations conveniently circumvent the need for a grid of position candidates or discretization of elevation and azimuth angles, which is present throughout the aforementioned grid-based works.

Early works in set-based shadow matching [3, 20, 21] struggled to match the computational efficiency of grid-based shadow matching and handled the buildings on a surface-by-surface basis with raster-based techniques that were difficult to scale to dense scenes. In our prior work [13,22], we independently derived set-based shadow matching and designed a novel set-based technique known as Zonotope Shadow Matching (ZSM). Unlike [20, 21], ZSM formulates the entirety of shadow matching with set-based objects. That is, even the buildings are stored as three-dimensional sets. Using the mathematics of constrained zonotopes, we efficiently compute the shadows online using fast vector concatenation operations.

ZSM then iteratively performs set intersection and subtraction to refine a set-based Area of Interest (AOI), i.e., the extent of the depicted ground (black) in Figures 1 and 2. A more complete discussion of the mathematics is included in [13, 22, 23]. Importantly, ZSM may be the algorithm of choice for users who require changes in scales (e.g., from the large scale of a coarse estimate to the small scale of a refined estimate), both online and offline computational efficiency, set-based continuum localization for downstream processing, and complete shadows in a minimal memory representation. In this way, we endeavor to introduce a new set-based paradigm to shadow matching.

To incorporate the GNSS pseudorange information, we leverage the set-based framework from ZSM to form a set-based method to process the GNSS pseudoranges in our recent work [24]. We then develop an iterative set-based filter that exploits the set-based form of the GNSS pseudoranges.

First, we propose Satellite-Pseudorange Consistency (SPC) objects that use the satellite position and pseudorange measurement to transfer set-based information in the two-dimensional receiver position domain among the satellites. The multipath and NLOS effects are notoriously difficult to efficiently model in urban settings [5,6]. The set-based SPC representation allows an efficient, compact, conservative representation of the uncertainty bounds without performing computationally expensive ray tracing. In essence, we trade off the precision of ray-tracing techniques for a more tractable and conservative set-based approach. As discussed in prior works [12, 15, 16], GNSS pseudoranges are most informative in the along-street direction. Second, we fuse a recent history of pseudorange measurements via an iterative filter. Our strategy shares some conceptual similarities with the hypothesis-domain integration in [15, 16] in that we integrate the information at the hypotheses level. However, we diverge significantly from these works with (1) using set-based projections rather than scoring over a grid, (2) explicitly reducing the mode ambiguity, (3) exploiting the slow shadow change compared to the pseudorange variability, and (4) not requiring an NLOS error distribution (e.g., past works assume skewed normal innovation vectors [16]). Our new method directly addresses the challenges of along-street inaccuracy and multi-modal ambiguity reduction identified by [12]. But, our method also significantly relaxes the requirements on shadow matching initialization, model discretization, and uncertainty quantification, all of which [12] considers important advances to shadow matching robustness for reliable urban GNSS localization.

Set-Based GNSS Pseudorange Processing

Unlike other works in urban localization, we leverage the four-dimensional conic geometry of the pseudorange measurement model to handle the pseudorange measurements in a fully set-based framework. Explorations of the four-dimensional conic geometry are largely constrained to the analytical GPS literature [25-28]. We combine the clock bias, environment bias (e.g., multipath) and additional noise into a single term called the range offset. The satellite-dependent range offset is approximately shared across satellites when the receiver clock bias dominates the range offset, the signal is LOS, or when the biases are similarly positively correlated across satellites. The receiver state reflects both the receiver position and overall range offset.

In shadow matching, we implicitly assume the shadows are cast onto a ground plane, as in Figure 1. As noted in [14], terrain aiding significantly improves urban localization, especially when processing pseudoranges. In terrain-aiding, we constrain the receiver state with information on the terrain model and a rough estimate of the receiver height.

This terrain information restricts the user state, thereby yielding a hyperboloid in the three dimensions. This represents all the receiver states consistent with the satellite position, corrected pseudorange and terrain. We call this the Satellite-Pseudorange Consistency (SPC).

The satellite elevation describes the trade between the horizontal plane of the ground versus the vertical. So, the shape of the hyperboloid changes with the satellite elevation angle where the slopes of the hyperboloid near the peak are more shallow as the elevation increases. We provide an example SPC hyperboloid in Figure 3. The zero range offset plane in Figure 3a illustrates the circle in horizontal position space (x, y) consistent with no range offset between the true range and corrected pseudorange.

At the scales of city blocks, the surface is nearly perfectly planar, even for satellites at high elevation angles (Figure 3b). So, we can linearize the hyperboloid about the center of the AOI. We denote the linearization of the SPC hyperboloid as the SPC plane. More mathematical details are provided in [24].

Iterative Set-Based Filter

We design a filtering framework that iteratively combines the information from ZSM and the SPC planes. We summarize the core intuition of the filter in Figure 4. We start with a uniform prior belief over the disjoint sets (magenta in Figure 4, matching Figure 1). First, we construct the SPC planes for each GNSS signal while fixing the user operating height. In blue and orange, we include the SPC planes of satellites 1 and 2 in the foreground of Figure 1. We include the SPC planes of three additional LOS satellites (green) for filter illustration purposes. A base WLS solution with terrain-aiding would find the point with minimum distance to the SPC planes, which can be far from the user position in urban settings. In contrast, we leverage the disjoint sets from ZSM to reduce the locations that the user can be. We form a mixture model to fuse information across satellites in the range offset domain. We weigh the satellites with the probability that the satellite is a LOS satellite. We then find the disjoint set where the fused information is most consistent to determine the more likely option of the disjoint sets from ZSM. When a few LOS satellites are present, this is largely where the LOS satellite SPC planes are nearest each other. In Figure 4, the left magenta set is more likely than the right magenta set because the LOS satellite SPC planes are closer and more overlapping in the range offset domain. From there, we iterate over multiple timesteps to better identify that the left set is the correct set for the user location. More mathematical details are provided in [24].

Performance with Real-World Data

We test the impact of both parts of our approach from [24]: (1) the LOS-weighted set-based SPC projections and (2) the iterative set-based filter. We assess the first part by comparing the SPC projections to shadow matching alone (i.e., ZSM). We further assess the first part by comparing the LOS-weighting in the mixture model to the unweighted mixture model. We test the second part of the approach by comparing a single-step filter to the iterative filter. We validate the filter performance with both a small and a large AOI.

1. Experiment platform and LOS classifier

We collected static-user data with the GNSS Logger App at 1 Hz on a Pixel 3 phone in the Financial District of San Francisco. The user is at the curb on Fremont Street north of Mission Street and outside the Salesforce West building.

The user environment is a significant urban canyon with three prominent glass-facade buildings and two prominent buildings with mixed concrete-glass facades, as illustrated in Figure 5. For ease of processing the signals from the GNSS Logger App, we only use GPS L1 signals though the method herein discussed can be extended to multi-constellation and multi-frequency in future works. We use the same 150 s timeseries throughout the analyses.

We trained a probabilistic LOS Classifier in MATLAB using the TU Chemnitz smartLoc dataset [29]. We trained on the Frankfurt Main Tower, Frankfurt West End Tower and Berlin Potsdamer Platz sections. We tested on the Berlin Gendarmenmarkt section. We use logistic regression and only input the C/N₀ data in the classifier. The final classifier has a 0.5-probability decision boundary at 34.5 dB-Hz between NLOS (negative class) and LOS (positive class). On the test set, we achieve a true positive rate of 69.8% and a true negative rate of 88.3%. Although the smartLoc dataset uses a ublox receiver, the logistic regression classifier generalizes well to the Pixel 3 phone. Further fine-tuning to adapt to the Pixel 3 phone would strictly improve classification but is outside the scope of this article.

2. Set-based shadow matching results

We use the ZSM algorithm detailed in [13, 22] to perform set-based shadow matching. Figure 6 illustrates the results of ZSM for a small AOI (120 m × 120 m in along and cross street directions) and a large AOI (300 m × 300 m in along and cross street directions). We observe two disjoint sets (i.e., a bimodal distribution) in the small AOI case with mode 2 (orange) as the correct mode. We incur six disjoint sets (i.e., six modes in the distribution) when we expand to a large AOI. Standard weighted least squares (WLS) incorrectly predicts mode 1 is the correct mode based on proximity throughout most of the experiment. For the large AOI, WLS incorrectly predicts modes 3, 4 and 5 at select time instances.

3. Set-based location ambiguity reduction

We first analyze how well the method components reduce the localization ambiguity for the case with two disjoint sets (Table 1).

The top performing combination is the proposed method (bottom right corner of Table 1) that starts with ZSM, uses the SPC projections, weights the measurements with the LOS classifier, and iteratively processes the pseudoranges over time.

We correctly arrive at the set with the user’s location in all timesteps with our proposed method for this data set. We also demonstrate how the iterative filter, SPC projections and LOS classifier work together to achieve the sought performance. First, the pseudorange information embedded in the SPC projections is critical in selecting the correct disjoint set. We identify the correct disjoint set in 78% of timesteps (from 0% in the uniform prior with ZSM alone) simply by including the SPC projections, even with a single-step filter. We improve to 96% when querying the LOS classifier to weigh the measurements. If we use an iterative design instead of the LOS classifier, we improve to 99%. Both these options improve the filter by rejecting the spurious NLOS and multipath-ridden outliers either by classification in the former case or by the temporal dispersion of the error in the latter case. We can reap the benefits of both options when we combine them in our proposed method because they work via different mechanisms. With the computational efficiency of the set-based method, the filter calculates the filter updates at roughly 3.7 ms per timestep and is fast enough for real-time operations. 

The second case with six disjoint sets is more difficult for the filter as it must reject five incorrect sets in the face of conservative approximations in the SPC projections. Still, we see the filter identifies the correct set in all timesteps for this dataset (Table 2). Indeed, we arrive at similar results to the case with two disjoint sets. As before, the SPC projections are the most significant improvement as we move from an inability to identify the correct set in ZSM alone to identifying the correct set in 76% of the timesteps with the GNSS pseudorange. However, to achieve the sought performance of 100%, we require input from the LOS classifier and the iterative filter design. 

The time to evaluate 150 timesteps only increases by roughly 200-300 ms (equivalently, 1-2 ms more per timestep) over the case with two disjoint sets. The method easily scales to larger AOIs with more multi-modal distributions.

Conclusion

We presented a new set-based paradigm for urban positioning. Our method reformulates past 3D mapping-aided techniques with computationally efficient set-based operations. In set-based shadow matching, we can fully represent the GNSS shadows without any discretization to better capture the shadow geometry. However, we retain similar issues of ambiguous locations where shadow matching produces multiple disjoint sets where the user could be located. To remedy this, we presented a fully set-based method to reduce location ambiguities in set-based shadow matching. Our proposed method had two key components: (1) processing GNSS shadows in a way conducive to set-based operations; and (2) iteratively filtering the pseudorange information via set-based operations to identify the most likely disjoint set from shadow matching. We validated our approach on smartphone data collected in the dense urban Financial District of San Francisco. We demonstrated both parts of the ambiguity reduction approach are critical to identifying the disjoint set that correctly matched the user location.

Our method is highly computationally efficient, and we can run the filter in roughly 3.7-5.4 ms per timestep depending on the number of disjoint sets. Given the 1 Hz data collection frequency in smartphones, this computational load is suitable for real-time operations. Our ongoing work includes leveraging higher-fidelity maps, quantifying the impact of classification or map uncertainty on the user’s positioning solution, and studying our set-based urban positioning paradigm in more diverse urban settings. 

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. DGE-1656518. We would like to thank Shubh Gupta for reviewing portions of this article. Lastly, we would like to thank the Google Android Location team for free and open-source data processing tools for smartphone GNSS data.

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Authors

Daniel Neamati is a Ph.D. student in the Department of Aeronautics and Astronautics at Stanford University. He received his bachelor’s degree in Mechanical Engineering, with a minor in Planetary Science, from the California Institute of Technology. His research interests include urban GNSS, geospatial information, autonomous decision-making and risk-aware localization.

Sriramya Bhamidipati is a robotics technologist at the Jet Propulsion Laboratory (JPL). Prior to JPL, she was a postdoctoral scholar in Aeronautics and Astronautics at Stanford University. She received her Ph.D. in Aerospace Engineering at the University of Illinois, Urbana-Champaign in 2021, where she also received her M.S. in 2017. She obtained her B.Tech. in Aerospace from the Indian Institute of Technology, Bombay, in 2015. Her research interests include space robotics, GPS, artificial intelligence and unmanned aerial systems.

Grace Gao is an assistant professor in the Department of Aeronautics and Astronautics at Stanford University. Before joining Stanford University, she was an assistant professor at the University of Illinois at Urbana-Champaign. She obtained her Ph.D. at Stanford University. Her research is on robust and secure positioning, navigation, and timing with applications to manned and unmanned aerial vehicles, autonomous driving cars, as well as space robotics.

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