skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
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.  more » « less
Award ID(s):
1845833 1815349
PAR ID:
10149437
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Sensors
Volume:
19
Issue:
24
ISSN:
1424-8220
Page Range / eLocation ID:
5402
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; (, Sensors)
    Smart health applications have received significant attention in recent years. Novel applications hold significant promise to overcome many of the inconveniences faced by persons with disabilities throughout daily living. For people with blindness and low vision (BLV), environmental perception is compromised, creating myriad difficulties. Precise localization is still a gap in the field and is critical to safe navigation. Conventional GNSS positioning cannot provide satisfactory performance in urban canyons. 3D mapping-aided (3DMA) GNSS may serve as an urban GNSS solution, since the availability of 3D city models has widely increased. As a result, this study developed a real-time 3DMA GNSS-positioning system based on state-of-the-art 3DMA GNSS algorithms. Shadow matching was integrated with likelihood-based ranging 3DMA GNSS, generating positioning hypothesis candidates. To increase robustness, the 3DMA GNSS solution was then optimized with Doppler measurements using factor graph optimization (FGO) in a loosely-coupled fashion. This study also evaluated positioning performance using an advanced wearable system’s recorded data in New York City. The real-time forward-processed FGO can provide a root-mean-square error (RMSE) of about 21 m. The RMSE drops to 16 m when the data is post-processed with FGO in a combined direction. Overall results show that the proposed loosely-coupled 3DMA FGO algorithm can provide a better and more robust positioning performance for the multi-sensor integration approach used by this wearable for persons with BLV. 
    more » « less
  2. ; ; (, Transportation Research Record: Journal of the Transportation Research Board)
    A resilient positioning, navigation, and timing (PNT) system is a necessity for the robust navigation of autonomous vehicles (AVs). A global navigation satellite system (GNSS) provides satellite-based PNT services. However, a spoofer can tamper the authentic GNSS signal and could transmit wrong position information to an AV. Therefore, an AV must have the capability of real-time detection of spoofing attacks related to PNT receivers, whereby it will help the end-user (the AV in this case) to navigate safely even if the GNSS is compromised. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection method using low-cost in-vehicle sensor data. We have utilized the Honda Research Institute Driving Dataset to create attack and non-attack datasets to develop a deep RL model and have evaluated the performance of the deep RL-based attack detection model. We find that the accuracy of the deep RL model ranges from 99.99% to 100%, and the recall value is 100%. Furthermore, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection. 
    more » « less
  3. ; ; ; (, 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. 
    more » « less
  4. ; ; ; (, Earth and Space Science)
    Abstract Due to the blockage of seawater, seafloor displacement cannot be directly measured by space geodesy. The combination of Global Navigation Satellite Systems‐acoustic ranging (GNSS‐A) has been used to overcome the electromagnetic barrier, so that a GNSS‐determined sea surface vessel's coordinates can be transformed to seafloor benchmarks in a global reference frame. Due to the high cost and science priorities, previous GNSS‐A studies mainly targeted relatively deep water and a minimum of three transponders were used to form an array, equivalent to a precision geodetic station. With recent developments in unmanned autonomous surface vessels, low cost GNSS‐A surveys are poised to become practical. Here we demonstrate that with a carefully designed surveying trajectory, Wave Glider‐based GNSS‐A surveying of a single transponder in shallow water can provide centimeter‐level accuracy on horizontal seafloor positioning, even if the sound speed model deviates from the actual value by a few meters per second. Results from a nine‐month experiment conducted at ∼54 m water depth show that the repeatability of the seafloor horizontal positioning is better than 2 cm. When conditions allow, the acoustic observations should be collected symmetrically about the transponder and data redundancies are recommended to reduce the error associated with time‐dependent variations in sound speed. 
    more » « less
  5. ; ; ; ; (, 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. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email