基于卡尔曼滤波的室内移动目标定位算法

Mobile target indoor positioning algorithm based on Kalman filter

针对室内定位算法受环境噪声影响严重,导致定位误差大的问题.首先在室内移动目标定位算法中引入卡尔曼滤波算法对原始采集数据进行滤波,能有效抑制定位过程中因数据采集误差引发的定位误差发散;然后通过kNN(k-Nearest Neighbor)算法估算待测目标的坐标;最后利用方位传感器对直线运动过程的定位点进行修正。通过3个阶段的优化可提高定位精度,仿真实验结果表明,基于卡尔曼滤波的室内移动目标定位算法的平均测量误差为0.648 7m,误差范围是0.1~1.9m,对于移动目标的路径测定也更加符合实际情况。

Indoor positioning algorithm it teriously affected by the environmental noise, leman filter algorithm it introduced into the mobile target indoor positioning algorithm to tupprest the divergence of the positioning error. Then, the kNN （k-Nearest Neighbor） algorithm s applied to estimate the positionisensor s used to correct the locating point of the linear motion process. The positioning accuracy could be improved through these three optimization stages. The emulation results show that the average positioning error of the mobialgorithm is only 0. 648 7 m, the error range is 0. 1-1. 9 m, and the path detetic.