多传感器行人航位推算方法和UKF融合算法

针对利用惯性测量单元进行行人航位推算（PDR）时，其定位误差会随时间累积的问题，提出了一种基于多传感器融合的室内行人航位推算方法；对于智能移动设备的低成本多传感器，设计了基于无迹卡尔曼滤波（UKF）的初始对准，设定4种阈值条件进行步伐状态检测；在行走过程中，针对步长和航向角误差累积的问题，利用基于UKF的零速度更新（ZUPT）对速度误差进行修正，零角速率更新（ZARU）和磁力计融合对航向角误差进行修正，从而有效提高了行人最终的位置精度。试验结果表明：使用该方法可以有效提高PDR位置精度，平均位置偏差占总路程的1.5%左右。

Research of Multi-sensor Pedestrian Dead Reckoning Method and UKF Fusion Algorithm

Aiming at the problem of pedestrian dead reckoning ( PDR) with inertial measurement unit and the positioning error will accumulate over time.A method of estimating indoor pedestrian space based on multi-sensor fusion is proposed.For low-cost multi-sensor of smart mobile device,the initial alignment based on the unscented Kalman filter ( UKF) is designed and the four threshold conditions are set to carry out the step state detection.In the process of walking,the problem of the accumulation of the step and the heading angle error is based on the zero speed update ( ZUPT ) of the UKF to correct the speed error, zero angle rate update ( ZARU ) and magnetometer fusion of the heading angle error correction, which effectively improve the pedestrian final position accuracy. The experimental results show that the method can effectively improve the average positional deviation of PDR position by about 1.5% of the total distance.