基于UWB和KF的无人车目标跟踪方法

Target tracking method for unmanned ground vehicle based on UWB and KF

针对无人车研究领域中的目标跟踪问题,提出了一种基于超宽带(UWB)技术和卡尔曼滤波(KF)定位算法的解决方法。以UWB采集的高精度距离信息为感知输入建立观测模型,分析目标运动特性建立系统状态模型;使用所设计的自适应平方根容积卡尔曼滤波(ASRCKF)定位算法,对目标位置进行定位跟踪。实验部分使用最小二乘估计(LSE)、扩展卡尔曼滤波(EKF)、无迹卡尔曼滤波(UKF)、容积卡尔曼滤波(CKF)等定位方法与提出方法进行对比,结果表明:所提出方法能提供更好的定位精度和稳定性,在动态跟踪环境下能达到15 cm的定位精度。

To solve the target tracking problem in the field of unmanned ground vehicle research,a new solution based on ultra wide band(UWB)technology and Kalman filtering(KF)positioning algorithm is proposed.The observation model is established by using ranging information collected by UWB as sensing input,and the system state model is established by analyzing target motion characteristic.A designed adaptive square root cubature Kalman filtering(ASRCKF)positioning algorithm is used to locate and track the target position.In experiments,the proposed method is compared with least squares estimation(LSE),extended Kalman filtering(EKF)and unscented Kalman filtering(UKF),and cubature Kalman filtering(CKF)positioning methods,result of experiments show that the proposed target tracking method provides better precision and stability than comparison methods,and can reach a positioning precision of 15 cm in dynamic tracking environment.