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

2025-06-04

AI Summary

In urban environments, non-line-of-sight (NLOS) multipath interference degrades GNSS carrier-phase–based velocity estimation in particle filters (PF), thereby compromising positioning accuracy. To address this, we propose a Rao-Blackwellized particle filter (RB-PF) that operates without integer ambiguity resolution. Our method decouples position and velocity states: velocity is estimated robustly via an extended Kalman filter (EKF), while position is estimated via particle filtering—marking the first integration of Rao-Blackwellization into ambiguity-free GNSS positioning. Additionally, we introduce a dynamic NLOS mitigation mechanism based on pseudorange residuals, preserving particle diversity while improving measurement quality. Real-world vehicle experiments in dense high-rise urban areas demonstrate centimeter-level positioning accuracy—significantly outperforming conventional PF and standard GNSS—along with stable tracking capability and high-confidence convergence.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Achieves centimeter-accurate GNSS positioning without integer ambiguity resolution
• Improves velocity and position estimation in urban NLOS multipath environments
• Enhances particle diversity and clustering for robust position tracking

Innovation

• Rao-Blackwellized particle filter for positioning
• Kalman filter enhances velocity estimation accuracy
• Rejects NLOS signals using pseudorange residuals