基于超宽带系统的双卡尔曼滤波定位算法  

A double-layer Kalman filter positioning algorithm based on ultra-wide band system

作　　者：刘倩芸 林敏 刘灏 于泽 郑立寅 Liu Qianyun;Lin Min;Liu Hao;Yu Ze;Zheng Liyin(School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学通信与信息工程学院,上海200444

出　　处：《电子技术应用》2023年第6期58-62,共5页Application of Electronic Technique

基　　金：国家重点研发计划项目(2019YFB2204500)。

摘　　要：针对室内复杂通信环境中对于移动目标的循迹需求,设计了一种削弱非视距误差的双卡尔曼滤波器,将经典卡尔曼滤波器与扩展卡尔曼滤波器进行级联,并引入一种根据残差分区调整卡尔曼滤波器协方差的区分误差方式,用于自适应调整经典卡尔曼滤波器的滤波增益,从而达到平滑观测值的作用,最终在扩展卡尔曼滤波后输出待测移动目标的位置信息,实现了移动目标的实时定位。在MATLAB上对该设计思路进行了仿真,在匀速运动模型下与现有的几种算法进行了精度的比较,所提出的双卡尔曼滤波器在仿真上能达到较高的循迹精度,均方根误差在视距情况下达到3 cm以内,非视距情况下达到10 cm以内。In order to track and locate the moving target in complex indoor environment,a double-layer Kalman filter(DKF)with weakening NLOS noises is designed,which cascades the classical Kalman filter(KF)and the Extended-Kalman filter(EKF).A method for distinguishing the noises is introduced into KF by adjusting the covariance according to the residual between the prediction and measurement.Through this method,the filter gain of KF is able to adjust adaptively,so that the distances mea‐sured by ultra-wide band(UWB)sensors can be smoothed and then input into the next EKF.Finally,the real-time positioning is achieved by outputting the position information of the moving target after EKF at each iteration.The algorithm is simulated on MATLAB,and the tracking accuracy is compared with several existing algorithms under the constant velocity(CV)model.The proposed DKF can achieve high accuracy within 3 cm in LOS environment and 10 cm in NLOS environment.

关 键 词：超宽带 卡尔曼滤波器 室内定位 非视距误差 

分 类 号：TN92[电子电信—通信与信息系统]