  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.
