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针对利用可穿戴式IMU对行人进行导航定位过程中,惯性器件产生累积漂移误差影响导航定位精度的问题,提出了一种基于改进扩展卡尔曼滤波(Improved Extended Kalman Filter,IEKF)的行人自主导航定位方法。该方法建立了融合人体运动特征的18维滤波模型,在IEKF中设计分段闭环平滑(Step Wise Closed loop Smoothing,SWCS)算法,消除跳变的修正采样点,提高了轨迹平滑度。利用自研的IMU传感器进行试验验证,结果表明该方法能够有效抑制惯性器件的发散,进一步提高了行人自主导航定位精度,并且不增加任何额外的硬件成本,对行人导航的研究具有实际应用价值。
In order to solve the problem that the drift of inertial devices affects the navigation and positioning accuracy during the process of navigating and positioning pedestrians using wearable IMUs, a pedestrian autonomous navigation and positioning system based on Improved Extended Kalman Filter (IEKF) is proposed method. The method builds an 18-dimensional filter model which integrates the human motion features. In the IEKF, a step-wise closed loop smoothing (SWCS) algorithm is designed to eliminate the revised sampling points of the transition and improve the track smoothness. Experimental results show that this method can effectively suppress the divergence of inertial devices, further improve the pedestrian autonomous navigation and positioning accuracy, and does not increase any additional hardware costs, and has practical application value for pedestrian navigation research.