A set-based urban positioning paradigm.

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.

References 

(1) Hsu, L.-T. (2017). Analysis and modeling GPS NLOS effect in highly urbanized area. GPS Solutions, 22(1):7.

(2) Zhu, N., Marais, J., Betaille, D., and Berbineau, M. (2018). GNSS position integrity in urban environments: A review of literature. IEEE Transactions on Intelligent Transportation Systems, 19(9):2762–2778.

(3) Groves, P. D. (2011). Shadow Matching: A new gnss positioning technique for urban canyons. NAVIGATION, 64(3):417–430. Groves, P. D. and Adjrad, M. (2019). Performance assessment of 3D-mapping–aided GNSS part 1: Algorithms, user equipment, and review. NAVIGATION, 66(2):341–362.

(4) Wang, L., Groves, P. D., and Ziebart, M. K. (2015). Smartphone shadow matching for better cross-street gnss positioning in urban environments. NAVIGATION, 68(3):411–433.

(5) Miura, S., Hsu, L.-T., Chen, F., and Kamijo, S. (2015). GPS error correction with pseudorange evaluation using three-dimensional maps. IEEE Transactions on Intelligent Transportation Systems, 16(6):3104–3115.

(6) Suzuki, T. (2016). Integration of GNSS positioning and 3D map using particle filter. In Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), pages 1296–1304.

(7) Van Diggelen, F. and Wang, J. (2020). Improving urban gps accuracy for your app.

(8) Wang, L., Groves, P., and Ziebart, M. (2013). Urban Positioning on a Smartphone. Inside GNSS, page 44–56.

(9) Irish, A., Iland, D., and Madhow, U. (2015). Urban Localization and 3D Mapping Using GNSS Shadows. Inside GNSS, page 60–66.

(10) Groves, P. (2016). It’s Time for 3D Mapping–Aided GNSS. Inside GNSS – Global Navigation Satellite Systems Engineering, Policy, and Design, page 50–56.

(11) Groves, P., Zhong, Q., Faragher, R., and Esteves, P. (2021). Supercorrelation Plus 3D Mapping-Aided GNSS. Inside GNSS.

(12) Groves, P. D., Wang, L., Adjrad, M., and Ellul, C. (2015). GNSS Shadow Matching: The Challenges Ahead. In Proceedings of the 28th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2015), pages 2421–2443.

(13) Bhamidipati, S., Kousik, S., and Gao, G. (2022). Set-valued shadow matching using zonotopes for 3d-map-aided GNSS localization. NAVIGATION: Journal of the Institute of Navigation, 69(4).

(14) Adjrad, M. and Groves, P. D. (2017). Enhancing Least Squares GNSS Positioning with 3D Mapping without Accurate Prior Knowledge. NAVIGATION, 64(1):75–91.

(15) Adjrad, M. and Groves, P. D. (2018). Intelligent Urban Positioning: Integration of Shadow Matching with 3D-Mapping-Aided GNSS Ranging. NAVIGATION, 71(1):1–20.

(16) Adjrad, M., Groves, P. D., Quick, J. C., and Ellul, C. (2019). Performance assessment of 3D-mapping-aided GNSS part 2: Environment and Mapping. NAVIGATION, 66(2):363–383.

(17) Zhong, Q. and Groves, P. D. (2021). Multi-Epoch 3D-Mapping-Aided Positioning using Bayesian Filtering Techniques. In Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), pages 195–225.

(18) Zhong, Q. and Groves, P. D. (2022). Multi-Epoch 3D-Mapping-Aided Positioning using Bayesian Filtering Techniques. NAVIGATION, 69(2).

(19) Wang, L. (2014). Kinematic GNSS shadow matching using a particle filter. In Proceedings of the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2014), pages 1907–1919.

(20) Yozevitch, R., Ben Moshe, B., and Levy, H. (2012). Breaking the 1 meter accuracy bound in commercial GNSS devices. In 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, pages 1–5.

(21) Yozevitch, R. and Ben Moshe, B. (2015). A Robust Shadow Matching Algorithm for GNSS Positioning. NAVIGATION, 62(2):95–109.

(22) Bhamidipati, S., Kousik, S., and Gao, G. (2021). Set-Valued Shadow Matching using Zonotopes. In Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), pages 2373–2390.

(23) Scott, J. K., Raimondo, D. M., Marseglia, G. R., and Braatz, R. D. (2016). Constrained zonotopes: A new tool for set-based estimation and fault detection. Automatica, 69:126–136.

(24) Neamati, D., Bhamidipati, S., and Gao, G. (2022). Set-based ambiguity reduction in shadow matching with iterative GNSS pseudoranges. In Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), pages 1093 – 1107.

(25) Bancroft, S. (1985). An Algebraic Solution of the GPS Equations. IEEE Transactions on Aerospace and Electronic Systems, AES-21(1):56–59.

(26) Abel, J. and Chaffee, J. (1991). Existence and uniqueness of GPS solutions. IEEE Transactions on Aerospace and Electronic Systems, 27(6):952–956.

(27) Chaffee, J. and Abel, J. (1994). On the exact solutions of pseudorange equations. IEEE Transactions on Aerospace and Electronic Systems, 30(4):1021–1030.

(28) Grafarend, E. W. and Shan, J. (2002). GPS Solutions: Closed Forms, Critical and Special Configurations of P4P. GPS Solutions, 5(3):29–41.

(29) Reisdorf, P., Pfeifer, T., Breßler, J., Bauer, S., Weissig, P., Lange, S., Wanielik, G., and Protzel, P. (2016). The problem of comparable GNSS results – an approach for a uniform dataset with low-cost and reference data. In Ullmann, M. and El-Khatib, K., editors, The Fifth International Conference on Advances in Vehicular Systems, Technologies and Applications, volume 5, page 8. ISSN: 2327-2058.

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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Identifying seismic signals in GNSS reference stations using machine learning.

TIM DITTMANN, UNIVERSITY OF COLORADO, BOULDER/EARTHSCOPE

JADE MORTON, UNIVERSITY OF COLORADO, BOULDER

Continuous GNSS reference stations represent stable benchmarks for unsung but critical roles in the broader infrastructure: defining reference frames and providing relative corrections, to name a few. But, what if the stable reference station is shaking? A large earthquake will release sufficient energy to permanently deform the earth and vibrate its crust and a coupled GNSS reference antenna¹. Relatively weaker seismic signals at or below the perceived GNSS noise floor still can be problematic for reference products. However, these GNSS seismic ground motions identified amongst GNSS ambient noise are valuable records for seismic monitoring and research. 

In this article, we provide some of our motivation with respect to the scientific utility of ground motion observations, the benefits of using GNSS as a source of these measurements, and the current role of GNSS in seismic monitoring. We then present our work selecting an optimal processing method to pair with a machine learning algorithm. This approach builds on existing stand-alone GNSS seismic awareness to enhance GNSS’ contribution to seismic hazard operations.

Informed Infrastructure Seismic Preparation

Data-driven research allows seismologists to use extensive data archives to account for the complexity of geophysical sources and signal paths of past, present and future events. Catalogs of historical earthquake ground motion data inform models of rupture and energy propagation for informed infrastructure seismic preparation. Real-time ground motion data enable a class of warning called earthquake early warning (EEW) [1]. A successful EEW system detects the earthquake and decides its extent AFTER the earthquake rupture to then alert a population BEFORE peak ground shaking travels to a populated area. This provides those in the impacted area maybe tens of seconds warning to take life-saving actions, whether that’s to duck and cover or to stop a train or medical procedure. Finally, near real-time ground motion data informs maps of shaking intensity for targeted post-earthquake response, and are appended to the historical catalogs as the newest data point for improved preparedness.

These ground motion measurements are the lens into the earthquake system and are traditionally sourced from dedicated, long-standing inertial seismic monitoring infrastructure. The use of higher rate GNSS for seismology, or GNSS seismology [2], was born out of the precision achieved through the seminal engineering of GPS/GNSS and progressed over the last two decades of GNSS seismic research.

Two reasons for including GNSS as a source of seismic observations emphasized in our analysis are:

Increased spatial availability: Existing inertial and geodetic networks were largely built and continue to operate independently. Inclusion of both sensor types increases the density of ground motion observations. Such a densification is particularly valuable in relatively sparser regions [3], such as Alaska, but also adds redundancy and resilience to all existing overlapped networks. 

Dynamic range: Inertial instruments are engineered with specific signal spectral characteristics of interest. As a result, inertial instruments are orders of magnitude more sensitive to weaker signals, including p-waves, the earliest smaller amplitude waves of earthquakes, and surface waves from events halfway around the globe. However, seismologists identified that in the nearfield of the largest events (M7.0+) that information encoded in the slower, longer period, large amplitude displacement signals is required to differentiate the magnitudes of these largest events. Traditional inertial sensors struggle to capture this information due to instrumental reasons. GNSS, with no geophysical upper bound, readily provides either direct or single integration large displacement or velocity measurements necessary for this magnitude differentiation at frequencies down to their permanent offsets. 

One important distinction: In this article, we discuss GNSS seismology and present the complementary nature of inertial and geodetic sensors as stand-alone instruments, as this is currently the primary global infrastructure status quo. However, another closely related area of development and promise is seismogeodesy, or tight local integration of these sensors into a single measurement [4].

The USGS ShakeAlert, the operational EEW system in the United States, ingests GNSS data from the western United States geodetic reference networks through a multi-agency and university collaboration to complement inertial data ingestion. Residents of the western U.S. will benefit from this current culmination of nearly two decades of multi-national GNSS seismology research and engineering. GNSS displacements have been included in USGS post-process fault models [5], but not yet included in shaking intensity products or operational ground motion models.

The potential measurement range of GNSS seismology has not yet been realized operationally in part because of inherent GNSS noise characteristics. GNSS ambient position noise is predominantly the aggregate of timing effects of GNSS radio signal propagation through the atmosphere, satellite and receiver oscillators and the antenna radio frequency environment. Each are location- and time-varying influences, and distinct from the zero-baseline inertial sensor noise seismologists are most accustomed to. Current methods for discriminating signal from noise adopt variations on low-pass filters or static or dynamic thresholds from seismology. To gain the desired sensitivity to signals, high-levels of false alerts from these methods are mitigated through correlating with the inertial system as well as additional GNSS locations. This mitigation adds points of failure and latency and reduces the overall range of valid measurements included. The performance opportunity for improved seismic monitoring and EEW is to rapidly include additional, potentially spatially diverse and unsaturated, ground motion signals from GNSS sources with minimal delay by accommodating the higher dimensionality of GNSS noise.

Comparing Geodetic Processing Methods

To address the presence of seismic signals in GNSS data, we began with an evaluation of two geodetic processing algorithms [6]. Currently, most operational systems and research approaches ingest one of the various methods of precise point positioning (PPP-AR) to make continuous estimates of antenna positions in a global reference frame. These PPP algorithms accomplish this precision at approximately sub-centimeter level using sophisticated error corrections models from multiple sources to estimate carrier phase ambiguities. GNSS seismology requires only relative topocentric motion; consequently, PPP absolute estimates are flattened to relative east, north and up components from a reference position. Time differenced carrier phase processing (TDCP) is a lightweight processing technique first applied to seismic applications by [7]. TDCP single differences epoch-wise carrier phase measurements remove correlated error sources (e.g. troposphere). After removing the satellite velocity, a broadcast ephemeris is acceptable for this, and a least squares system of equations of all observed satellites resolves a topocentric antenna velocity vector and clock drift estimate.

We compared the relative signal to noise of PPP to TDCP to determine our processing method. For our noise estimates, we assembled a dataset of event-free 1 Hz GNSS observational data tracked by multiple receiver types, using a variety of antennas in diverse RF environments, across a hemispheric scale to account for a wide range of noise sources (Figure 3). For PPP processing, we used the UNAVCO/EarthScope PPP solutions from the Trimble RTX software [8]. For TDCP processing, we used the open-source python package SNIVEL [9], which uses GPS only, broadcast ephemeris and the narrow-lane L1/L2 carrier phase combination. From the event-free processed time-series, we estimated a stochastic noise for each station-processing method pair without cleaning or filtering the data. We used these thresholds to establish a statistical noise threshold distribution across this network wide dataset to represent the ambient noise distributions.

For our reference signals, we used empirical scaling laws that relate peak dynamics, earthquake magnitude and radius from the hypocenter. These scaling laws [10, 11] are derived from existing earthquake catalogs and useful for rapid magnitude estimation; the PPP-derived peak ground displacement (PGD) scaling law is part of the current ShakeAlert geodetic contribution. We estimated a signal-to-noise (SNR) metric using PGD or peak ground velocity (PGV) derived from respective scaling laws as our signal reference related to respective ambient noise levels. This SNR metric was estimated over a range of radii from a range of earthquake magnitudes. We found TDCP is more likely than PPP to detect the intermediate magnitude earthquakes (Figure 4) and has additional benefits of being a computational light-weight, open source processing method that doesn’t require external corrections for ephemeris or error source models.

Identifying Seismic Events in GNSS Timeseries with Machine Learning

The results of our ambient processing method comparison indicated TDCP offered lightweight geodetic processing with increased sensitivity, yet still demonstrated unacceptable operational false alarm rates from our statistical threshold. The complexity of GNSS noise coupled with the variability in seismic signals encouraged us to look to an alternative detection approach. Machine learning (ML) is now an ubiquitous tool in data science. Earth scientists have leveraged algorithms developed for natural language processing or image classification and applied them to a range of challenging problems [12] difficult to represent in physics-based models.

We set up a data-driven ML pipeline to train, validate and test a binary classification machine learning model [13]. The foundation of our data-driven experiment is a catalog of 1,706 5Hz TDCP velocity waveforms processed from the UNAVCO/EarthScope geodetic archive concurrent with 80 earthquakes ranging from magnitude of 4.9 to 8.2. An event-free 5Hz TDCP dataset from 30 minute windows prior to the events was included to ensure sufficient noise samples in training and testing for model generalization of these imbalanced datasets. We used 5Hz data to boost signal energy and reduce the likelihood of aliasing, and set a radius of sensitivity for each event as a function of magnitude given our previous sensitivity analysis.

Feature engineering in ML is the process of applying relevant domain knowledge to the ML model for successful classification. We evaluated several feature engineering strategies: the most effective strategy consisted of a combination of time- and lower frequency-domain features (1-30s period) extracted from overlapping 30 second windows. The three topocentric components’ features were labeled through visual inspection and concatenated into a single binary sample and label for each timestamp.

We chose a random forest classifier as our ML algorithm and adopted a nested cross validation technique in our classification training and testing  (Figure 5). This validation strategy allowed us to make training/testing splits of our data on the 80 discrete earthquakes, and evaluate our model’s performance on unseen events in training. We optimized the model on a balance of sensitivity scores and false positives using its F-1 score. A traditional accuracy metric on highly imbalanced classification data, such as our earthquake catalog, is typically not descriptive of performance (e.g., for events happening <1% of the time, you can miss 100% of the events and still be >99% accurate).

The random forest classifier achieved a 90% true positive rate of the station-event pairs (Figure 6) across the entire catalog. The stand-alone classifier substantially outperformed the existing threshold and filtering (e.g. short term average over long term average, STA/LTA) detection methods as shown in Figure 7. These performance results from the classifier’s combination of time- and frequency-domain features into its decision criteria could readily improve GNSS contribution to operational seismic monitoring and ground motion catalogs. Additional investigations in deeper learning models will likely enable researchers to ask more sophisticated questions.

Finally, we tested the timing of the classifier when run once per second on the 5Hz samples of test data not used in training. We found the classifier typically had its first detection approximately at or immediately after the anticipated seismic secondary wave arrival (Figure 8). This result explains our model, or alignment of our results with our domain knowledge that explains the model’s performance. The model did not detect the weaker seismic primary wave arrivals, but instead identified the larger, lower frequency ground motions of the seismic secondary and surface waves. This result also offers implications for GNSS and inertial complimentary hazard monitoring, particularly EEW when timing and accuracy are critical.

Conclusions

Ground motion observations are the data currency of earthquake hazard preparation, monitoring and research. Continuous high-rate GNSS reference stations offer an alternative source that expands the dynamic range of inertial-based ground motion measurements in the nearfield of the largest, most devastating earthquakes and spatially complements existing inertial infrastructure. Complex GNSS noise signatures have bounded operational incorporation of GNSS in these hazard systems. However, alternative, lightweight processing (TDCP) paired with machine learning (random forest classifier) offers enhanced confidence in signal from noise discrimination to confidently include these ground motion measurements in operational systems with minimal false alerting and without external corrections services. The global proliferation of higher-rate GNSS reference stations to support a variety of disparate position, navigation and timing applications could all become medium to large earthquake seismometers, alerting reference station users in addition to contributing to the global seismic monitoring systems. Furthermore, embedding TDCP processing coupled with ML at high rates (>=5Hz) at the network edge will enhance the next generation of geodetic sensor networks to stream higher rate velocities for seismic monitoring or archive denser raw observables for addressing future seismic research objectives. 

Acknowledgments

We would like to thank Yuinxang (Leo) Liu, Kathleen Hodgkinson, Brendan Crowell, David Mencin and Glen Mattioli. We acknowledge the open geodetic data available from the National Science Foundation GAGE facility operated by EarthScope and the open-source software used for GNSS velocity processing and their analysis, including GNSS velocity processing and machine learning libraries.

References

[1] R. Allen and D. Melgar, “Earthquake Early Warning: Advances, Scientific Challenges, and Societal Needs,” Annual Review of Earth and Planetary Sciences, 2019. 

[2] K. Larson, “GPS seismology,” Journal of Geodesy, 2008. 

[3] R. Grapenthin, M. West and J. Freymueller, “The Utility of GNSS for Earthquake Early Warning in Regions with Sparse Seismic Networks,” Bulletin of the Seismological Society of America, 2017.

[4] Goldberg, D. E., and Y. Bock (2017), Self-contained local broadband seismogeodetic early warning system: Detection and location, J. Geophys. Res. Solid Earth, 122, 3197–3220, doi:10.1002/2016JB013766.

[5] D. E. Goldberg, P. Koch, D. Melgar, S. Riquelme and W. L. Yeck, “Beyond the Teleseism: Introducing Regional Seismic and Geodetic Data into Routine USGS Finite‐Fault Modeling,” Seismological Society of America, 2022.

[6] T. Dittmann, K. Hodgkinson, J. Morton, D. Mencin and G. Mattioli, “Comparing Sensitivities of Geodetic Processing Methods for Rapid Earthquake Magnitude Estimation,” Seismological Research Letters, 2022.

[7] G. Colosimo, M. Crespi and A. Mazzoni, “Real‐time GPS seismology with a stand‐alone receiver: A preliminary feasibility demonstration,” Journal of Geophysical Research: Solid Earth, 2011.

[8] R. Leandro, H. Landau, M. Nitsschke and e. al., “RTX positioning: The next generation of cm-accurate real-time GNSS positioning,” Proceedings of the 24th international technical meeting of the satellite division of the Institute of Navigation, 2011.

[9] B. W. Crowell, “Near-field strong ground motions from GPS-derived velocities for 2020 Intermountain Western United States Earthquakes,” Seismological Research Letters, 2021.

[10] D. Melgar, B. Crowell, J. Geng, R. Allen and Y. Bock, “Earthquake magnitude calculation without saturation from the scaling of peak ground displacement,” Geophysical Research Letters, 2015.

[11] R. Fang, J. Zheng, J. Geng, Y. Shu and C. Shi, “Earthquake Magnitude Scaling Using Peak Ground Velocity Derived from High-Rate GNSS Observations,” Seismological Research Letters, 2020.

[12] K. Bergen, P. Johnson, M. V. de Hoop and G. Beroza, “Machine learning for data-driven discovery in solid Earth geoscience,” Science, 2019.

[13] T. Dittmann, Y. Liu, J. Morton and D. Mencin, “Supervised Machine Learning of High Rate GNSS Velocities for Earthquake Strong Motion Signals,” Journal of Geophysical Research: Solid Earth, 2022.

Authors

Tim Dittmann is a data scientist at the EarthScope consortium and doctoral candidate at the Ann and H.J. Smead Aerospace Engineering Sciences at the University of Colorado, Boulder.

Y. Jade Morton is Helen and Hubert Croft Professor and Director of the Colorado Center for Astrodynamics Research in the Ann and H. J. Smead Aerospace Engineering Sciences Department at the University of Colorado Boulder. She received a Ph.D. in Electrical Engineering (EE) from Penn State. She is a member of the U.S. Space-based PNT Advisory Board, a recipient of the AGU SPARC award, the IEEE PLANS Kershner Award, and the Institute of Navigation’s (ION) Burka, Thurlow, Kepler, and distinguished service Awards. Dr. Morton is a Fellow of ION, RIN and the IEEE.

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In the wake of the latest COP session on international climate change, and as the world continues to deal with the effects of a warming planet, researchers say more and better data can help ameliorate the damage and provide answers on how to respond to it.

GIS giant Esri hosted a webinar on Nov. 30 to discuss how imagery from satellites and drones, combined with artificial intelligence and cloud storage, can help hold United Nations members and other states accountable in creating a more sustainable world.

The answer: By generating more data, taking more measurements, making the data more accessible and putting it into products that are easy to understand by even non-scientists.

Panelist Steve Brumby, cofounder and CEO of Impact Observatory, said he sees a pending era of “radical transparency” where people have the information they need to understand the choices their governments make.

“In the next 12 months, we’re moving from a situation where anyone can expect to get an annual map from one year ago … to having essentially continuous monitoring,” Brumby said. “Anybody in the world should be able to say, here’s my area of interest … let me understand how this area is changing.”

He said anyone should be able to get information on how much land has been urbanized, how much coastland has eroded, and other measurements. Such data should no longer be just the realm of experts, but “there should be no barrier to people getting that type of data.”

Panelist “Stinger” Gerald Guala, a program scientist at NASA, said a record number of Earth-observing satellites means more data than ever.

“We’ve got a lot of new data sets coming in,” he said. “More data is always better than less data. … we’ve got a bigger fleet [of satellites] right now that address climate change directly than we’ve ever had before. We’re going to have a lot more information in the future.”

Panelist Amos Desjardins, the data inspection, enhancement and delivery section chief at the U.S. Department of Agriculture, said his agency has about 7 petabytes of data, nine million frames of imagery, including one data set of a county in Wisconsin dating back to 1951.

“I always like to pivot and look backwards a little bit, and see where have we come from, what has changed over time?” he said. “Going forward, how can we collect imagery that will be useful?” The USDA maps the entire United States every other year (half one year, half the next) at resolutions as low as 15 centimeters, “and make that publicly available … so researchers and the general public can utilize that information.”

Brumby said the results from COP27, the 27th United Global Climate Change Conference, were disappointing to the conservation community in terms of practical results for fighting global warming.

With data from satellites, aircraft and on the ground, “things are changing faster than some of the models we hoped were correct not too long ago. The impacts on everyday life are already becoming undeniable, and there’s going to be a period where better decision making is not going to be some sort of luxury … it’s just going to be something people have to adapt to.”

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The European Union Agency for the Space Programme (EUSPA) has published its Earth Observation (EO) & GNSS Market Report, an outgrowth of its annual GNSS Market Report now that the agency has also taken on Earth observation among its administrative responsibilities. The Report is compiled and written for all those making these technologies part of their business plan and developing downstream applications.

In 2021, GNSS and EO downstream market generated over 200 billion euros revenues and are set to reach almost half a trillion over the next decade. EO and GNSS data have become increasingly important in the big data realm and paradigm responding to natural and man-made disasters, curbing the spread of disease and strengthening a global supply chain, among many other goals.

The Report provides analytical information on the dynamic GNSS and EO markets, along with in-depth analyses of the latest global trends and developments through illustrated examples and use cases. Using advanced econometric models, it also offers market evolution forecasts of GNSS shipments or EO revenues spanning to 2031.

With a focus on Galileo/EGNOS and Copernicus, the report highlights the essential role of space data across 17 market segments including,

• Agriculture; Aviation and Drones;
• Biodiversity, Ecosystems and Natural Capital;
• Climate Services; Consumer Solutions, Tourism, and Health;
• Emergency Management and Humanitarian Aid;
• Energy and Raw Materials; Environmental Monitoring;
• Fisheries and Aquaculture; Forestry;
• Infrastructure;
• Insurance and Finance;
• Maritime and Inland Waterways;
• Rail;
• • Road and Automotive;
• Urban Development and Cultural Heritage;
• and Space.

Some report highlights:

• Global annual GNSS receiver shipments will reach 2.5 bn units by 2031, dominated by the applications falling under the Consumer Solutions, Tourism and Health segment contributing roughly to 92% of global annual shipments;

• In EO, aside from the largest markets like Agriculture, Urban Development and Cultural Heritage or Energy and Raw Materials, the Insurance and Finance segment is expected to experience the fastest growth over the next decade (21 % of CAGR) for both EO data and value-added service revenues;

• From a supply perspective, the European Industry holds over 41% of the global EO downstream market and 25 % of the global GNSS downstream market.

“The flagship EU Space Programme, driven by Galileo and EGNOS on one side and Copernicus on the other, has become a major enabler in the downstream space application market. As a user-oriented agency, we provide this inside information on markets evolution to our users, being innovators, entrepreneurs, investors, academic researchers, chipset manufacturers, or simply anyone who looks into space to bring value to their activities. The added value and key differentiators of European GNSS and EO are showcased, both separately and in synergy with each other. I know that the report will be of great use and inspiration for those who are contributing to the EU economic growth,” concluded EUSPA Executive Director Rodrigo da Costa.

The report is available for download here.

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Trimble has rolled out the latest version of its core geospatial automated monitoring software, Trimble 4D Control version 6.3. The software provides automated movement detection to enable informed decisions about infrastructure for surveying, construction and monitoring professionals.

Version 6.3 adds new capabilities for the software to work in combination with the Trimble SX Series Scanning Total Stations’ advanced imaging and measurement capabilities. This version also supports vibration and weather-station sensors and a streamlined workflow between the Trimble Access Monitoring Module in the field with the new T4D Access Edition used in the office.

Enhancements to the geospatial monitoring software provide increased accuracy; simplified sensor data collection, reporting and alarms; and make it possible to seamlessly move from semi-automated to fully automated monitoring on a project.

Integrated with the SX Series Scanning Total Station, T4D brings VISION imaging technology and high-accuracy Lightning 3DM technology for more accurate measurements, enabling a more dense target placement on linear corridors such as rail tracks, tunnels, roads and bridges. A live video feed makes it possible to better understand site conditions, manage target placement remotely and capture images for use with T4D visual inspection capabilities. These images can be compared over time and viewed next to the displacement or movement charts. This enables users to identify the potential cause of displacement and record movement changes over time.

Vibration and Weather

With the upgrade, vibration sensors from Syscom allow surveying, civil and geotechnical engineers to easily combine geodetic and geotechnical information supporting high-frequency and event-based vibration information. This data is often used for mandatory reporting on civil and infrastructure projects.

Integration with the Vaisala weather station analyzes the impact of environmental conditions such as temperature, rainfall, wind and atmospheric pressure in combination with other geospatial and geotechnical monitoring information, which is useful for slope stability analysis in mining, landslide and dam monitoring operations.

From Semi- to Fully Automated Monitoring

Automated, seamless transfer of field data from the monitoring module to software in the office makes it possible to scale monitoring operations from a semi-automated to fully automated monitoring system while maintaining the continuity of historical data in the same charts and reports.

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A Soyuz launcher operated by Arianespace and commissioned by ESA lifted off with the pair of 715 kg satellites from French Guiana on December 5. The two join 26 Galileo satellites in the orbiting constellation that now provide Initial Services.

The satellites will spend the coming weeks being manoeuvred into their final working orbit at 23 222 km using their onboard thrusters, at the same time as their onboard systems are gradually checked out for operational use – known as the Launch and Early Operations Phase.

ESA, tasked with designing, developing, procuring, testing, and qualifying the Galileo system and overseeing its technical evolution, recently led an upgrade of Galileo’s worldwide ground control segment. This makes it possible for the satellites’ LEOP to be run by the Galileo operator, SpaceOpal, from Galileo’s own control centre in Oberpfaffenhofen, Germany, for the first time – rather than requiring an external mission control site. The LEOP operations are being run under the responsibility of the EU Agency for the Space Programme (EUSPA).

ESA Director of Navigation Paul Verhoef comments: “Today’s liftoff marks the 11th Galileo launch of operational satellites in ten years: a decade of hard work by Europe’s Galileo partners and European industry, over the course of which Galileo was first established as a working system then began Initial Services in 2016. With these satellites we are now increasing the robustness of the constellation so that a higher level of service guarantees can be provided.”

“The recent ground control segment update permits mission controllers to oversee more Galileo satellites at the same time,” adds Pascal Claudel, Chief Operating Officer of EUSPA, tasked with managing the Galileo operations and service provision.

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GNSS-R measures many phenomena near Earth’s surface, including ocean winds, flooding, land cover (snow, ice, vegetation), soil moisture, and topography by means of reflected GNSS signals. NASA’s recent CYGNSS mission demonstrated the broad utility of the GNSS-R technique. GeoOptics is working with JPL to deploy an advanced operational version, offering dramatically enhanced performance in a small, low-cost package. This collaboration is funded jointly by GeoOptics, the U.S. Air Force, and NASA.

With launches beginning next year, CICERO-2 will form a unified Earth observatory allowing governments, industry, and individual stakeholders to monitor and prepare for the many impacts of climate change.

“In today’s environment, in which precision Earth sensing is becoming ever more critical, GeoOptics is deploying a flexible observatory made up of dozens of small satellites. The real time services will satisfy a broad range of needs for government and civil users around the world,” said Alex Saltman, CEOof GeoOptics.

CICERO-2 small satellites will also operate:

Global Precipitation Watch – Monitoring heavy precipitation using Polarimetric radio occultation (RO), an advanced remote sensing technique pioneered by GeoOptics’ collaborators at NASA’s Jet Propulsion Laboratory (JPL) and the Spanish PAZ mission.

Triple RO – Profiling of atmospheric temperature, pressure, density, and other key properties by means of GNSS–RO. First proposed by company founder Tom Yunck while he was at JPL, GNSS-RO offers unrivalled measurement precision and is an essential contributor to global weather forecasting.

For GeoOptics strategic partner Climavision, a weather data provider, these innovations will enable customers to manage significant risks in a time of global change. “With these new developments in remote sensing technologies from GeoOptics, we’ll be able to further enhance our climate and weather prediction capabilities,” said Chris Goode, CEO and Co-Founder of Climavision. “Through the combination of advanced RO profiles, GNSS-R data about surface conditions and our proprietary gap-filling radar network data, we’ll help customers in weather-sensitive industries see weather like never before and give them the tools and data to make informed critical decisions.”

GeoOptics will later extend the system to a range of new applications, including precise mapping of Earth’s gravitational field, which has been named a top NASA Earth science priority for the next decade. This measurement shows the imprint of climate-related movement of water and other key changes in the earth. With internal investment and nearly $4 million from NASA, GeoOptics has devised a unique system architecture for daily gravity mapping with clusters of small satellites. This patented technique promises to improve gravity sensing 20-fold over current methods at a fraction of the cost.

Under the umbrella of the National Oceanographic Partnership Program (NOPP), GeoOptics is also designing a radar instrument to observe ocean vector winds, topography, soil moisture, and a variety of other surface properties with patented multi-satellite radar techniques. NOPP is seeking to sponsor a trial flight of GeoOptics’ Cellular Ocean Altimetry/Scatterometry Technology (COAST) within the next two years.

Tom Yunck, GeoOptics’ Chief Technology Officer, said, “These advanced remote sensing applications – from basic RO to advanced radar and gravity mapping – exploit shared micro technologies that fit in the palm of one’s hand. Each new function builds naturally upon the previous, yielding prodigious observing capacity in a low-cost system of great simplicity and reliability.”

“CICERO-2 is designed to help provide high priority NOAA climate and weather monitoring observations, as ranked by the NOAA Space Platform Requirements Working Group (SPRWG),” said Conrad C. Lautenbacher (Vice Admiral, USN ret.), Executive Chairman of GeoOptics and former NOAA Administrator. “It can also play a key role in supporting crucial Defense Department satellite weather data requirements.”

GeoOptics’ CICERO satellites continue to provide global profiles of the Earth’s atmosphere. In February 2021, the National Oceanic and Atmospheric Administration (NOAA) selected GeoOptics to provide the first commercial satellite data to be included in their operational forecasts. In 2020, GeoOptics was selected by NOAA to lead an end-to-end design study for their next-generation low-orbiting weather satellite system, planned to come online later this decade, building in part on RO and GNSS-R technologies.

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The trial follows US federal executive order 13905 aimed at strengthening national resilience through PNT services, including protecting critical infrastructure such as electrical power grid and communication networks from rising cyber threats. By harnessing ADVA’s flexible OSA 5420 Series, designed with assured PNT (aPNT) technology, UrsaNav has shown that eLoran can provide a new layer of protection and significantly boost timing resilience and security.

“The technology enables ADVA’s grandmaster clock to receive UTC timing from the eLoran system for a period of several days with the same accuracy and stability as GPS. Of course, this capability is extensible to other GNSS as well. eLoran is far less vulnerable to unintentional jamming and spoofing disruptions or intentional attacks, thereby delivering nanosecond precision with even more resilience,” said Charles Schue, CEO, UrsaNav.

UrsaNav’s latest trial harnessed the OSA 5420 Series grandmaster clock with inbuilt GNSS receiver. Timing stability from GPS was measured for several days. This was then replaced with eLoran for the same period with no loss of stability. The test was conducted indoors where GNSS signals are not usually available, potentially extending the availability of precise UTC timing to many more environments.

“Commercially available GNSS jammers and spoofers are easy and cheap for attackers to construct. That’s part of the reason why we’re seeing a growing number of incidents across the world of blocked or misleading signals,” commented Nir Laufer, VP, product line management, Oscilloquartz, ADVA. “If power utilities, enterprises, service providers and governments continue to rely on GNSS alone, it’s only a matter of time before the consequences become very serious. That’s why we’re committed to tackling GNSS vulnerabilities with advanced technologies like our ePRTC offering, cesium atomic clocks and our optical timing channel solution. Now that UrsaNav has demonstrated the power of our OSA 5420 Series to utilize eLoran in the event of outages, we have another very important tool to ensure the quality and availability of time-sensitive services.”

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Swift’s wide-area corrections solution, a hybrid of precise point positioning (PPP) and real-time kinematic (RTK), delivers wide-area corrections with a low density of reference stations, fast convergence and centimeter-level accuracy, delivered via the cloud. The Skylark service targets the integrity and availability required by autonomous, mass-market and mobile applications. Skylark is GNSS hardware-agnostic, giving customers a choice in which GNSS sensor is used and enabling subscribers across industries to benefit from higher accuracy.

“We believe that Swift’s high-precision positioning solution further empowers our business capabilities in mobility space and contributes to the expansion of business coverage into smart vehicles,” said Hiromichi Matsuda, Executive Officer, Business Exploration & Development at KDDI CORPORATION. “The accuracy afforded from precise positioning unlocks opportunities for a multitude of businesses and industries and adds value to our customers in Japan.”

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For decades, NASA has played a leading role in using Earth-observing satellites to collect the data required to feed numerical weather prediction models. CYGNSS continues that work, using a remote sensing technique called “GPS signal scattering” to see through heavy rain to estimate the strength of surface winds in the inner cores of hurricanes.

“CYGNSS has been a pioneering mission that has given us new insight into the dynamics of rapidly intensifying tropical cyclones,” said Karen St. Germain, director of NASA’s Earth Science Division. “CYGNSS is also a powerful tool for flood detection on land and ocean microplastic debris detection – that’s the kind of added value we love to see, and it’s paving the way for more science that will have significant societal benefits.”

The measurements from CYGNSS are useful for research in algorithm development, analysis to assist with future modeling efforts, and Earth system process studies.

Further operations will enable new research looking at long-term climate variability and increase the sample size of extreme events that can assist with modeling and forecasting. CYGNSS satellites continue to take 24/7 measurements of ocean surface winds, both globally and in tropical cyclones, which can be used to study meteorological processes and improve numerical weather forecasts. On land, the satellites take continuous measurements of flood inundation and soil moisture that are used in hydrological process studies and for disaster monitoring.

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