NLOS Error Mitigation Using Weighted Least Squares and Kalman Filter in UWB Positioning

TL;DR

This paper introduces WLS-RKF, a novel method combining weighted least squares and Kalman filtering to identify and mitigate NLOS errors in UWB positioning, achieving high accuracy without prior NLOS knowledge.

Contribution

The paper proposes a new WLS-RKF approach that effectively mitigates NLOS errors in UWB positioning without needing prior NLOS distribution data.

Findings

Achieves 5cm positioning accuracy in simulations and experiments.

Effectively identifies NLOS conditions using Mahalanobis distance hypothesis testing.

Mitigates NLOS bias without prior knowledge of NLOS signal features.

Abstract

In wireless positioning systems, non-line-of-sight (NLOS) is a challenging problem. NLOS causes great ranging bias and location error, so NLOS mitigation is essential for high accuracy positioning. In this letter, we propose the Weighted-Least-Squares Robust Kalman Filter (WLS-RKF) for NLOS identification and mitigation. WLS-RKF employs a hypothesis test based on Mahalanobis distance for NLOS identification, and updates the corresponding Kalman filter using the WLS solution. It requires no prior knowledge about NLOS distribution or signal features. We perform simulations and experiments for ultra-wideband (UWB) positioning in various scenarios. The results confirm that WLS-RKF effectively mitigates NLOS error and achieves 5cm positioning accuracy.