自适应NLOS信号抑制联合卡尔曼滤波的UWB定位算法  

UWB POSITIONING ALGORITHM BASED ON ADAPTIVE NLOS SIGNAL SUPPRESSION AND KF

作　　者：曾纪钧 温柏坚 张小陆 Zeng Jijun;Wen Bojian;Zhang Xiaolu(Guangdong Power Grid Co.,Ltd.,Guangzhou 510600,Guangdong,China)

机构地区：[1]广东电网有限责任公司,广东广州510600

出　　处：《计算机应用与软件》2025年第2期355-360,共6页Computer Applications and Software

基　　金：南方电网科技项目(037800KK52190006)。

摘　　要：由于非视距(Non-Line of Sight,NLOS)信号的存在,基于卡尔曼滤波(Kalman Filter,KF)的超宽带室内定位方法会出现定位精度下降的问题,提出一种自适应NLOS信号抑制联合KF的UWB定位算法。对UWB接收信号进行建模,并估计得到NLOS信号的协方差矩阵;利用该协方差矩阵对接收信号进行“白化”抑制;利用KF进行室内定位,同时针对KF滤波发散、误差较大的问题,利用RBF神经网络对误差进行在线修正,提升滤波性能。实验结果表明,该方法在NLOS环境下能够获得亚米级的定位精度,并具有较强的环境适应性。Due to the existence ofnon-line of sight(NLOS)signals,the positioning accuracy of the traditional ultra-wideband indoor positioning method based on Kalman filtering dropped significantly.In response to this situation,a UWB positioning algorithm based on adaptive NLOS signal suppression and Kalman filter(KF)is proposed.The algorithm modeled and analyzed the UWB received signal,and estimated the covariance matrix of the NLOS signal.The covariance matrix was used to"whiten"the received signal.The KF was used to perform indoor positioning under the background of Gaussian white noise.At the same time,the neural network was used to correct the error online to improve the filtering performance.Experimental results show that this method can obtain sub-meter positioning accuracy in NLOS environment,and has strong robustness.

关 键 词：超宽带 室内定位 卡尔曼滤波 协方差矩阵 信号白化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]