基于卡尔曼滤波和粒子滤波融合的UWB室内定位算法

UWB Indoor Positioning Algorithm Based on Kalman Filter and Particle Filter Fusion

基于超宽带(ultra-wideband,UWB)室内定位技术得到了广泛的发展,然而,在LOS(line-of-sight)和NLOS(non-line-of-sight)环境下的UWB的测距信息均存在不同程度的误差,因此,提出了一种改进的卡尔曼滤波算法对UWB原始数据进行平滑处理;之后提出卡尔曼滤波(Kalman filters and particle filters,KPF)和粒子滤波融合的算法。通过卡尔曼滤波得到的状态量和误差协方差进行粒子采样,克服了传统粒子滤波进行粒子采样时的运动学模型与实际运动不相符的缺点,大幅减少了粒子退化的现象。经过实验,该算法在LOS和NLOS环境中的定位精度分别提升了20.6%和15.6%。

Indoor positioning technology based on ultra-wideband(UWB)has been widely developed.However,the measurement of UWB in LOS(line-of-sight)and NLOS(non-line-of-sight)environments There are different degrees of error in the distance information,so an improved Kalman filter algorithm is proposed to smooth the UWB original data;then a Kalman filter and particle filter(KPF)particle filter and Kalman filter fusion algorithm is proposed.Particle sampling is carried out through the state quantity and error covariance obtained by Kalman filtering,which overcomes the disadvantage that the kinematic model of traditional particle filtering does not match the actual motion,and greatly reduces the phenomenon of particle degradation.After experiments,the positioning accuracy of the algorithm in LOS and NLOS environments is improved by 20.6%and 15.6%,respectively.