基于改进鲁棒自适应容积卡尔曼滤波的 UWB 定位。

UWB Localization Based on Improved Robust Adaptive Cubature Kalman Filter.

机构信息

School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China.

出版信息

Sensors (Basel). 2023 Feb 28;23(5):2669. doi: 10.3390/s23052669.

DOI:10.3390/s23052669

PMID:36904872

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10007378/

Abstract

Aiming at the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate kinematic model in ultra-wideband (UWB) systems, this paper proposed an improved robust adaptive cubature Kalman filter (IRACKF). Robust and adaptive filtering can weaken the influence of observed outliers and kinematic model errors on filtering, respectively. However, their application conditions are different, and improper use may reduce positioning accuracy. Therefore, this paper designed a sliding window recognition scheme based on polynomial fitting, which can process the observation data in real-time to identify error types. Simulation and experimental results indicate that compared to the robust CKF, adaptive CKF, and robust adaptive CKF, the IRACKF algorithm reduces the position error by 38.0%, 45.1%, and 25.3%, respectively. The proposed IRACKF algorithm significantly improves the positioning accuracy and stability of the UWB system.

摘要

针对超宽带（UWB）系统中存在的非视距（NLOS）观测误差和运动学模型不准确的问题，本文提出了一种改进的鲁棒自适应容积卡尔曼滤波（IRACKF）算法。鲁棒滤波和自适应滤波可以分别削弱观测异常值和运动学模型误差对滤波的影响。但是，它们的应用条件不同，不当使用可能会降低定位精度。因此，本文设计了一种基于多项式拟合的滑动窗口识别方案，可以实时处理观测数据以识别误差类型。仿真和实验结果表明，与鲁棒容积卡尔曼滤波（RCKF）、自适应容积卡尔曼滤波（ACKF）和鲁棒自适应容积卡尔曼滤波（RAACKF）相比，IRACKF 算法分别将位置误差降低了 38.0%、45.1%和 25.3%。所提出的 IRACKF 算法显著提高了 UWB 系统的定位精度和稳定性。