Reliable GNSS Localization Against Multiple Faults Using a Particle Filter Framework

TL;DR

This paper introduces a particle filter framework with a Gaussian Mixture Model likelihood to improve GNSS localization accuracy and reliability in urban environments with multiple measurement faults, enhancing system availability and reducing false alarms.

Contribution

It presents a novel particle filter-based approach that robustly estimates vehicle position and system availability amidst multiple GNSS measurement faults using GMM likelihoods.

Findings

Achieves smaller horizontal positioning errors compared to existing methods.

Determines system availability with fewer false alarms.

Reduces integrity risk in urban GNSS navigation.

Abstract

For reliable operation on urban roads, navigation using the Global Navigation Satellite System (GNSS) requires both accurately estimating the positioning detail from GNSS pseudorange measurements and determining when the estimated position is safe to use, or available. However, multiple GNSS measurements in urban environments contain biases, or faults, due to signal reflection and blockage from nearby buildings which are difficult to mitigate for estimating the position and availability. This paper proposes a novel particle filter-based framework that employs a Gaussian Mixture Model (GMM) likelihood of GNSS measurements to robustly estimate the position of a navigating vehicle under multiple measurement faults. Using the probability distribution tracked by the filter and the designed GMM likelihood, we measure the accuracy and the risk associated with localization and determine the availability of the navigation system at each time instant. Through experiments conducted on challenging simulated and real urban driving scenarios, we show that our method achieves small horizontal positioning errors compared to existing filter-based state estimation techniques when multiple GNSS measurements contain faults. Furthermore, we verify using several simulations that our method determines system availability with smaller probability of false alarms and integrity risk than the existing particle filter-based integrity monitoring approach.