论文标题
NLOS误差使用加权最小二乘和UWB定位中的Kalman滤波器缓解
NLOS Error Mitigation Using Weighted Least Squares and Kalman Filter in UWB Positioning
论文作者
论文摘要
在无线定位系统中，非视线（NLOS）是一个具有挑战性的问题。 NLOS会导致偏差和位置误差引起很大的范围，因此NLOS缓解对于高精度定位至关重要。在这封信中，我们提出了加权最高方分可靠的卡尔曼滤波器（WLS-RKF），以供NLOS识别和缓解。 WLS-RKF采用基于Mahalanobis距离进行NLOS识别的假设检验，并使用WLS解决方案更新相应的Kalman滤波器。它不需要有关NLOS分布或信号特征的事先了解。我们在各种情况下对超宽带（UWB）定位进行模拟和实验。结果证实WLS-RKF有效减轻了NLOS误差并达到5cm定位精度。
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.
