一种基于卡尔曼滤波的超宽带室内定位算法

为了减小室内环境中障碍物对超宽带(UWB)传感器测距结果的影响,提出了一种基于卡尔曼滤波(KF)的超宽带室内定位算法.利用超宽带接收信号的信噪比区分视距和非视距环境,给出了超宽带传感器测距性能最小二乘标定模型,减小测距系统误差;判断相邻测距差分是否在阈值范围内,否则用卡尔曼滤波先验估计替代后验估计处理测距结果,由此减弱多径效应和非视距误差对测距的影响;用扩展卡尔曼滤波器(EKF)实现室内定位.实验结果表明:算法在复杂室内环境中可达到亚米级的动态实时定位精度.

An UWB indoor positioning algorithm based on Kalman filtering

In order to eliminate the effects of indoor obstacles lead to ultra-wideband(UWB) ranging result, present an UWB indoor localization algorithm based on Kalman filtering. By using signal noise ratio of UWB to distinguish line of sight from non-line of sight environment,give least squares calibration models for ultra-wideband sensor ranging performance to reduce the distance measuring system error. To weaken multipath effects and effect of non-line of sight error on ranging,use Kalman filtering priori estimation to replace posterior estimation to process ranging result. Extended Kalman filter is used to achieve indoor positioning. Experimental results prove that,the algorithm has sub-meter high dynamic realtime positioning precision in complex indoor environments.