基于改进PDR与RSSI融合的定位算法

Positioning Algorithm Based on Improved PDR and RSSI Fusion

在室内定位系统中,基于接收信号强度指示(RSSI)测距定位系统接收到的信号会因环境的不确定性出现不可预测的随机变化,行人航位推算(PDR)定位系统存在错误地估计传感器的参数及左右脚运动不一致等产生累积误差的问题。针对上述问题,提出一种基于改进PDR与RSSI融合的定位算法,根据PDR定位的递归特性校正估计传感器的参数,同时进行左右脚坐标数据融合,在此基础上将扩展卡尔曼滤波器(EKF)作为RSSI和PDR定位的融合滤波器,以降低PDR累计误差,从而提高定位精度,获得系统的最优定位结果。实验结果表明,该融合定位算法有效地提高了定位精度。

In the indoor positioning system,based on the received signal strength indication(RSSI)the signal received by the ranging and positioning system will undergo unpredictable random changes due to the uncertainty of the environment.The pedestrian dead reckoning(PDR)positioning system has incorrectly estimate of the sensor's parameters and the inconsistent movement of the left and right feet produced cumulative error problem.In view of the above problems,a positioning algorithm based on the fusion of improved PDR and RSSI is proposed.The parameters of the estimated sensor are corrected according to the recursive characteristics of PDR positioning,and the data of the left and right foot coordinates are fused.On this basis,the Extented Kalman filter(EKF)will be used as a fusion filter for RSSI and PDR positioning to reduce the cumulative error of PDR,thereby improving positioning accuracy and obtaining the optimal positioning result of the system.Experimental results show that the fusion positioning algorithm effectively improves the positioning accuracy.