Tightly Coupled Rao-Blackwellized Particle Filter for GNSS-Only Positioning in Urban Environments Without Ambiguity Resolution

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 measurements, which are vulnerable to non-line-of-sight (NLOS) multipath 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 positioning performance compared to existing methods, confirming its effectiveness under degraded GNSS conditions.