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1. Toward Autonomous Detection of Anomalous GNSS Data Via Applied Unsupervised Artificial Intelligence

   https://doi.org/10.1142/S1793351X22400025  

   Dye, Mike; Stamps, D. Sarah; Mason, Myles; Saria, Elifuraha (March 2022, International Journal of Semantic Computing)

   Artificial intelligence applications within the geosciences are becoming increasingly common, yet there are still many challenges involved in adapting established techniques to geoscience data sets. Applications in the realm of volcanic hazards assessment show great promise for addressing such challenges. Here, we describe a Jupyter Notebook we developed that ingests real-time Global Navigation Satellite System (GNSS) data streams from the EarthCube CHORDS (Cloud-Hosted Real-time Data Services for the geosciences) portal TZVOLCANO, applies unsupervised learning algorithms to perform automated data quality control (“noise reduction”), and explores autonomous detection of unusual volcanic activity using a neural network. The TZVOLCANO CHORDS portal streams real-time GNSS positioning data in 1[Formula: see text]s intervals from the TZVOLCANO network, which monitors the active volcano Ol Doinyo Lengai in Tanzania, through UNAVCO’s real-time GNSS data services. UNAVCO’s real-time data services provide near-real-time positions processed by the Trimble Pivot system. The positioning data (latitude, longitude and height) are imported into the Jupyter Notebook presented in this paper in user-defined time spans. The positioning data are then collected in sets by the Jupyter Notebook and processed to extract a useful calculated variable in preparation for the machine learning algorithms, of which we choose the vector magnitude for further processing. Unsupervised K-means and Gaussian Mixture machine learning algorithms are then utilized to locate and remove data points (“filter”) that are likely caused by noise and unrelated to volcanic signals. We find that both the K-means and Gaussian Mixture machine learning algorithms perform well at identifying regions of high noise within tested GNSS data sets. The filtered data are then used to train an artificial intelligence neural network that predicts volcanic deformation. Our Jupyter Notebook has promise to be used for detecting potentially hazardous volcanic activity in the form of rapid vertical or horizontal displacement of the Earth’s surface. 

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2. Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?

   https://doi.org/10.3390/s19245402  

   Medina, Daniel; Li, Haoqing; Vilà-Valls, Jordi; Closas, Pau (December 2019, Sensors)

   Navigation problems are generally solved applying least-squares (LS) adjustments. Techniques based on LS can be shown to perform optimally when the system noise is Gaussian distributed and the parametric model is accurately known. Unfortunately, real world problems usually contain unexpectedly large errors, so-called outliers, that violate the noise model assumption, leading to a spoiled solution estimation. In this work, the framework of robust statistics is explored to provide robust solutions to the global navigation satellite systems (GNSS) single point positioning (SPP) problem. Considering that GNSS observables may be contaminated by erroneous measurements, we survey the most popular approaches for robust regression (M-, S-, and MM-estimators) and how they can be adapted into a general methodology for robust GNSS positioning. We provide both theoretical insights and validation over experimental datasets, which serves in discussing the robust methods in detail. 

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3. A Proposed Framework for UAS Positioning in GPS-Denied and GPS-Spoofed Environments

   https://doi.org/10.1109/ICNS60906.2024.10550601  

   Burbank, Jack L; Foust, Landon; Greene, Trevor; Kaabouch, Naima (April 2024, IEEE)

   NA (Ed.)

   Unmanned Aerial Systems have become ubiquitous and are now widely used in commercial, consumer, and military applications. Their widespread use is due to a combination of their low cost, high capability, and ability to perform tasks and go places that are not easy or safe for humans. Most UAS platforms are dependent on Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), to provide positioning information for navigation and flight control. Without reliable GPS signals, the flight path cannot be trusted, and flight safety cannot be assured. However, GPS is vulnerable to several types of malicious attacks, including jamming, spoofing, or physical attacks on the GPS constellation itself. Additionally, there are environments in which GPS reception is not always possible, a key example being urban canyon areas where line-of-site to the GPS satellite constellation may be blocked or obscured by large obstacles such as buildings. Numerous methods have been proposed for position estimation in GPS denied environments. However, these methods have significant limitations and typically exhibit poor performance in certain environments and scenarios. This paper analyzes the strengths and weaknesses of existing alternate positioning methods and describes a framework where multiple positioning solutions are jointly employed to construct an optimal position estimate. The proposed framework aims to reduce computation complexity and yield good positioning performance across a wide variety of environments. 

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4. Evaluating GNSS Navigation Availability in 3-D Mapped Urban Environments

   https://doi.org/10.1109/PLANS46316.2020.9109929  

   Nagai, Kana; Fasoro, Titilayo; Spenko, Matthew; Henderson, Ron; Pervan, Boris (April 2020, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS))

   This research project aims to achieve a future urban environment where people and self-driving cars coexist together while guaranteeing safety. To modify the environment, our first approach is to understand the limitations of GPS/GNSS positioning in an urban area where signal blockages and reflections make positioning difficult. For the evaluation process, we assume reasonable integrity requirements and calculate navigation availability along a sample Chicago urban corridor (State Street). We reject all non-line-of-sight (NLOS) that are blocked and reflected using a 3-D map. The availability of GPS-only positioning is determined to be less than 10% at most locations. Using four full GNSS constellations, availability improves significantly but is still lower than 80 % at certain points. The results establish the need for integration with other navigation sensors, such as inertial navigation systems (INS) and Lidar, to ensure integrity. The analysis methods introduced will form the basis to determine performance requirements for these additional sensors. 

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5. Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

   https://doi.org/10.3390/s21041250  

   Medina, Daniel; Li, Haoqing; Vilà-Valls, Jordi; Closas, Pau (February 2021, Sensors)

   null (Ed.)

   Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness. 

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