Tightly Coupled Rao-Blackwellized Particle Filter for GNSS-Only Positioning in Urban Environments without Ambiguity Resolution
Daiki Niimi, An Fujino, Taro Suzuki, Junichi Meguro
AI summary
Problem
Urban multipath and signal blockages disrupt carrier-phase continuity and integer ambiguity resolution, while previous ambiguity-free particle filters rely on Doppler-based velocity estimates that are highly vulnerable to these errors, causing positioning failure.
Approach
The method directly incorporates raw Doppler measurements into a Kalman filter to jointly estimate velocity and receiver clock drift, augmented by a robust Kalman filter using Student’s t-distribution and particle-wise non-line-of-sight rejection to filter out multipath outliers.
Key results
- Direct integration of raw Doppler measurements into the Kalman filter for continuous velocity estimation
- Particle-wise NLOS rejection and Student’s t-distribution-based robust filtering to mitigate multipath outliers
- Superior centimeter-level positioning accuracy across six challenging urban test scenarios
- Elimination of integer ambiguity resolution while maintaining robustness under signal blockage
Why it matters
Provides a reliable, high-precision localization solution for autonomous vehicles and mobile robots operating in degraded urban GPS environments where traditional RTK-GNSS fails.
Abstract
This paper presents a tightly coupled Rao- Blackwellized particle filter (TC-RBPF) for global navigation satellite system (GNSS) positioning that eliminates the need for carrier-phase integer ambiguity resolution. The previously proposed loosely coupled RBPF (LC-RBPF) approach uses carrier-phase residuals to estimate particle likelihoods, enabling positioning without integer ambiguity resolution. However, the position estimation accuracy depends on the performance of the state transition. The previous approach estimates velocity using a Kalman filter (KF) based on least-squares Doppler measure- ments, which are vulnerable to non-line-of-sight (NLOS) multi- path errors. This often leads to complete positioning failure in urban environments. To overcome these limitations, the proposed TC-RBPF tightly integrates raw Doppler measurements into the KF. This enables consistent estimation of both velocity and receiver clock drift within a time-series framework. Furthermore, a robust KF based on Student’s t-distribution and particle-wise NLOS rejection using double-differenced pseudorange residuals are introduced to mitigate the impact of outliers. Together, these mechanisms enhance outlier robustness and transition reliability. Experimental evaluations in six challenging urban scenarios demonstrate that the proposed method achieves superior posi- tioning performance compared to existing methods, confirming its effectiveness under degraded GNSS conditions.