Enhancement of Low-cost GNSS Localization in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters

TL;DR

This paper presents a novel method using Rao-Blackwellized particle filters to improve low-cost GNSS localization accuracy in connected vehicle networks by fusing data and map matching, significantly reducing errors.

Contribution

It introduces a Rao-Blackwellized particle filter approach for joint bias and position estimation, enhancing localization accuracy without expensive infrastructure.

Findings

Reduced estimation error by 50%

Decreased estimation variance by two orders of magnitude

Effective mitigation of multipath biases

Abstract

An essential function for automated vehicle technologies is accurate localization. It is difficult, however, to achieve lane-level accuracy with low-cost Global Navigation Satellite System (GNSS) receivers due to the biased noisy pseudo-range measurements. Approaches such as Differential GNSS can improve the accuracy, but usually require an enormous amount of investment in base stations. The emerging connected vehicle technologies provide an alternative approach to improving the localization accuracy. It has been shown in this paper that localization accuracy can be enhanced by fusing GNSS information within a group of connected vehicles and matching the configuration of the group to a digital map to eliminate the common bias in localization. A Rao-Blackwellized particle filter (RBPF) was used to jointly estimate the common biases of the pseudo-ranges and the vehicles positions. Multipath biases, which are non-common to vehicles, were mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation was exploited through the prediction-update process. The proposed approach was compared to the existing static and smoothed static methods in the intersection scenario. Simulation results show that the proposed algorithm reduced the estimation error by fifty percent and reduced the estimation variance by two orders of magnitude.