Tram Positioning with Map-Enabled GNSS Data Reconciliation

TL;DR

This paper introduces a map-enabled GNSS data reconciliation method using an iterated extended Kalman filter to improve tram localization accuracy in urban environments with multipath signal issues, demonstrated on simulated and real data.

Contribution

It presents a novel IEKF-based approach for tram positioning that is computationally efficient and improves localization accuracy over baseline methods in challenging urban scenarios.

Findings

Improved root-mean-squared error in simulated data

Enhanced real-world tram localization accuracy

Potential for further refinement with advanced outlier rejection techniques

Abstract

This paper presents an approach to tackle the problem of tram localization through utilizing a custom processing of Global Navigation Satellite System (GNSS) observables and the track map. The method is motivated by suboptimal performance in dense urban environments where the direct line of sight to GNSS satellites is often obscured which leads to multipath propagation of GNSS signals. The presented concept is based upon the iterated extended Kalman filter (IEKF) and has linear complexity (with respect to the number of GNSS measurements) as opposed to some other techniques mitigating the multipath signal propagation. The technique is demonstrated both on a simulated example and real data. The root-mean-squared errors from the simulated ground truth positions show that the presented solution is able to improve performance compared to a baseline localization approach. Similar result is achieved for the experiment with real data, while treating orthogonal projections onto the tram track as the true position, which is unavailable in the realistic scenario. This proof-of-concept shows results which may be further improved with implementation of a bank-of-models method or $\chi^2$-based rejection of outlying GNSS pseudorange measurements.