WiFi与行人航迹推算自适应无迹卡尔曼滤波融合定位算法

WiFi and Pedestrian Dead Reckoning Adaptive Unscented Kalman Filter Fusion Positioning Algorithm

针对WiFi信号易受环境波动和存在多径效应现象,行人航位推算(pedestrian dead rockoning,PDR)系统中传感器模块精度有限,且存在累积误差问题,因此提出了一种加入自适应调整因子的改进无迹卡尔曼滤波(unscented Kalman filter,UKF)融合算法,该算法利用残差r k的理论协方差与实际协方差的差异作为条件,引入调整因子ρ调整状态向量和观测向量的协方差进而调整卡尔曼增益参数。实验结果表明平均定位误差为1.35 m,最大定位误差为2.23 m。定位误差在1.5 m以内的概率达到了约80%,相比标准UKF算法在1.5 m以内的概率约为22%,提高了约58%。该算法提高了室内定位的定位精度,增强了定位的稳定性。

In view of the susceptibility of WiFi signals to environmental fluctuations and multipath phenomena,in pedestrian dead reckoning(PDR)system,the accuracy of the sensor module is limited,resulting in a problem of cumulative error.An improved unscented Kalman filter(UKF)with adaptive adjustment factor was proposed.The fusion algorithm used the difference between the theoretical covariance and the actual covariance of the residual r k as a condition to introduce the adjustment factorρto adjust the state vector and the observed covariance to fine-tune the Kalman gain parameter.The experimental results show that the average positioning error is 1.35 m and the maximum positioning error is 2.23 m.The probability of positioning error within 1.5 m reaches about 80%,which is an increase of about 58%compared to 22%of the probability of standard UKF algorithm within 1.5 m.The algorithm improves the indoor positioning accuracy and enhanced the stability of positioning.