Multipath Detection from GNSS Observables Using Gated Recurrent Unit

Abstract

One of the most used Position, Navigation, and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit Line-Of-Sight (LOS) and increase position ambiguity. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as the transportation of organs that are time-sensitive. Therefore, techniques to mitigate Non-Line-Of-Sight (NLOS) interference are required for improved positioning accuracy. This paper proposes a Gated Recurrent Unit-based (GRU) multipath detection algorithm that uses pseudorange, ephemerides, Doppler shift, Carrier-To-Noise Ratio (C/N0), and elevation data from each satellite to determine whether multipath is present. Signals from the satellite classified as multipath are then flagged and ignored for Position, Velocity, and Timing (PVT) calculations until they are deemed as LOS. The classification algorithm is developed and tested on Spirent GSS7000 to generate GNSS Radio Frequency (RF). OKTAL-SE Sim3D is used to simulate urban canyon environments in which signals propagate from the satellite to the receiver. RF signals are then transmitted to a Ublox F9P GNSS receiver that can receive GPS and GLONASS signals which are processed to output PVT information. The data collected is used to train the GRU to classify received signals as no multipath or multipath.From performance evaluation, GRU outperforms decision tree, K-Nearest Neighbor (KNN) classifiers, and Support Vector Machines (SVM). Furthermore, comparing GRU with SVM, a 50% increase in accuracy is observed with a 95% error of 0.85 m for GRU compared to 1.78 m for SVM.

Uncertainty-based Sensor Fusion Architecture using Bayesian-LSTM Neural Network

“…An example integrity monitoring technique used in aviation is Receiver Autonomous Integrity Monitoring (RAIM), which uses pseudorange measurements, given by the receiver, to detect faulty signals and excludes them from PVT calculations [9]. However, this approach can only detect one fault at a time and requires 5 satellites to be in Line-Of-Sight (LOS) for the fault detection [2]. This may be difficult to achieve in urban canyons with a large field of view being obscured and high probability of interference.…”

“…One of the most prevalent challenges facing mounting interests in Unmanned Aerial Vehicles (UAVs) is the ability for autonomous navigation in densely populated environments, ensuring mission safety and availability [1]. Due to the nature of urban canyons, GNSS-based positioning struggle with occlusions caused by large buildings, as direct Line-Of-Sight (LOS) is required for accurate positioning [2]. Other factors, including intentional (jamming and spoofing) or unintentional interference, can lead to catastrophic failures.…”

Realism-Oriented Design, Verification, and Validation of Novel Robust Navigation Solutions

“…As can be seen from [1,2], AI models can be used to increase the navigation robustness of UAVs under multipath and NLOS conditions [3]. Moreover, given the complexity that such navigation solutions must be performing in, a realistic yet repeatable development, testing, and validation environment is required.…”