Robust Position Estimation by Rao-Blackwellized Particle Filter without Integer Ambiguity Resolution in Urban Environments

TL;DR

This paper introduces a robust GNSS positioning method using Rao-Blackwellized particle filtering that improves accuracy in urban environments without needing integer ambiguity resolution, effectively handling NLOS multipath errors.

Contribution

The study applies Rao-Blackwellization to particle filtering for GNSS, enhancing velocity and position estimation accuracy without ambiguity resolution, especially in urban NLOS conditions.

Findings

Achieved centimeter-level positioning accuracy in urban tests.

Effectively rejected NLOS multipath signals during velocity estimation.

Outperformed conventional particle filter and GNSS methods in urban environments.

Abstract

This study proposes a centimeter-accurate positioning method that utilizes a Rao-Blackwellized particle filter (RBPF) without requiring integer ambiguity resolution in global navigation satellite system (GNSS) carrier phase measurements. The conventional positioning method employing a particle filter (PF) eliminates the necessity for ambiguity resolution by calculating the likelihood from the residuals of the carrier phase based on the particle position. However, this method encounters challenges, particularly in urban environments characterized by non-line-of-sight (NLOS) multipath errors. In such scenarios, PF tracking may fail due to the degradation of velocity estimation accuracy used for state transitions, thereby complicating subsequent position estimation. To address this issue, we apply Rao-Blackwellization to the conventional PF framework, treating position and velocity as distinct states and employing the Kalman filter for velocity estimation. This approach enhances the accuracy of velocity estimation and, consequently, the precision of position estimation. Moreover, the proposed method rejects NLOS multipath signals based on the pseudorange residuals at each particle position during the velocity estimation step. This process not only enhances velocity accuracy, but also preserves particle diversity by allowing particles to transition to unique states with varying velocities. Consequently, particles are more likely to cluster around the true position, thereby enabling more accurate position estimation. Vehicular experiments in urban environments demonstrated the effectiveness of proposed method in achieving a higher positioning accuracy than conventional PF-based and conventional GNSS positioning methods.