1 research outputs found
Reliable GNSS Localization Against Multiple Faults Using a Particle Filter Framework
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.Comment: 13 pages, 7 figures Submitted to IEEE Transactions on Intelligent
Transportation Systems (T-ITS