地图匹配辅助的KF-PF室内定位算法模型

An Indoor Positioning System Based on Map-Aided KF-PF Module

针对使用智能手机进行行人航迹推算（pedestrain dead reckoning,PDR）时航向角漂移,定位精度不高,误差累积的问题,提出了一种地图匹配辅助的卡尔曼滤波-粒子滤波（Kalman filter-particle filter,KF-PF）多重滤波算法对PDR算法进行优化。在传统PDR算法的基础上,使用KF融合陀螺仪数据和地图信息解算航向角,然后采用基于地图匹配的粒子滤波算法对轨迹结果进行处理。实验结果表明,该方法消除了航向角误差过大对定位结果的影响,在提高室内定位的灵活性的同时增强了定位的稳定性和精度,并通过地图匹配减少了传统粒子滤波采样点数,降低了运算量,使其在手机平台上实时运行成为可能。

Due to the drift of yaw,low accuracy and accumulative erroring in the procedure of using smartphone to realize Pedestrain dead reckoning algorithm,a map-aided KF-PF multi-filter algorithm is used to optimize PDR algorithm.Based on the traditional PDR algorithm,a Kalman Filter,fusing output of gyroscope and cartographic information primarily,is used to get the orientation,then using the map-matching particle filter to process the route results.The experimental results show that the flexibility of indoor positioning is improved,in the meanwhile,the stability and preciseness of the positioning results is enhanced and the algorithm can eliminate the error of the drift of yaw.Compared with the traditional particle filter,the map-matching particle filter can decrease the number of particles and computation burden effectively,which makes the possibility for realizing the real-time indoor localization.