行人自主导航定位的多级滤波方法
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摘要
针对携带式IMU对行人进行导航定位时,惯性器件的误差随时间增长累积变大,影响导航准确性的问题,研究了一种多级滤波的方法:零速检测并利用扩展卡尔曼滤波进行零速校正后,再通过室内几何布局特征划分矢量区,利用投影匹配算法判定位置的最优解,来确定人行走轨迹。用自主研制的MIMU行人导航模块做了场地实验,结果表明:该方法相对目前单级卡尔曼滤波的惯性导航方法,能够很好地抑制惯性导航误差积累,且不会出现行人轨迹穿墙问题,定位精度为0.9%,具有理论和实际的意义。
In the process of navigation and location of a pedestrian for a wearable IMU, the inertial device generates an accumulated drift error affecting the navigation and location accuracy of pedestrian navigation. A multi-stage filtering method is studied: after the zero-speed detection and the zero-speed correction based on Extended Kalman Filter are carried out, the vector domain is divided by the indoor geometric layout features, and the projected matching model is used to determine the optimal coordinates of the nodes to get the trajectory. Using the self-developed MIMU pedestrian navigation module, a field experiment was conducted. The experimental results show that this method can suppress the accumulation of inertial navigation error, which is better than the current single-stage Kalman filter inertial navigation method. There is no pedestrian trajectory passing through the wall.The navigation and location accuracy of pedestrian navigation is improved and the location accuracy is 0.9%. The research has theoretical and practical significance.
In the process of navigation and location of a pedestrian for a wearable IMU, the inertial device generates an accumulated drift error affecting the navigation and location accuracy of pedestrian navigation. A multi-stage filtering method is studied: after the zero-speed detection and the zero-speed correction based on Extended Kalman Filter are carried out, the vector domain is divided by the indoor geometric layout features, and the projected matching model is used to determine the optimal coordinates of the nodes to get the trajectory. Using the self-developed MIMU pedestrian navigation module, a field experiment was conducted. The experimental results show that this method can suppress the accumulation of inertial navigation error, which is better than the current single-stage Kalman filter inertial navigation method. There is no pedestrian trajectory passing through the wall.The navigation and location accuracy of pedestrian navigation is improved and the location accuracy is 0.9%. The research has theoretical and practical significance.
引文
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[2]Abdulrahim K,Hide C,Moore T,et al.Integrating low cost IMU with building heading in indoor pedestrian navigation[J].Journal of Global Positioning Systems(S1446-3164),2011,10(1):30-38.
[3]于飞.步幅和建筑方向辅助的行人导航算法[J].哈尔滨工程大学学报,2016,37(3):408-413.Yu Fei.Pedestrian navigation algorithm assisted by stride and building direction[J].Harbin Engineering Newspaper,2016,37(3):408-413.
[4]Lin W C,Sun S W,Cheng W H,et al.Compass fusion:High Precision indoor people localization and identification[C]∥HH Chu.Proc.of the 11th Annual International Conference on Mobile Systems,Applications,and Services,Taipei,Taiwan,China.NY,USA:ACM New York,2013:497-498.
[5]谷阳.基于鞋载惯性传感器的地图辅助人员定位方法[J].系统工程与电子技术,2015,37(7):1633-1637.Gu Yang.Map-assisted personnel positioning method based on shoe inertial sensors[J].Systems Engineering and Electronics,2015,37(7):1633-1637.
[6]S Rajagopal.Personal dead reckoning system with shoe mounted inertial sensors[D].Stockholm,Sweden:Royal Institute Technology,2008.
[7]Grover Robert,P Y C Hwang.Introduction to random signals and applied Kalman filtering[M].Canada:Hamilton Printing Company,1992.
[8]胡安冬.一种基于地图匹配辅助行人航迹推算的室内定位方法[J].测绘科学技术学报,2014,31(5):530-537.Hu Andong.An indoor positioning method based on map matching for assisting pedestrian track estimation[J].Journal of Surveying and Mapping Science,2014,31(5):530-537.
[9]王凯龙.基于微机电系统的室内行人航向修正算法[J].科学技术与工程学报,2018,18(6):263-267.Wang Kailong.The indoor pedestrian heading correction algorithm based on MEMS[J].Journal of Science Technology and Engineering,2018,18(6):263-267.
[10]Shin E H.Estimation techniques for low cost inertial navigation[D].Canada:University of Calgary,2005.
[11]赵正平.典型MEMS和可穿戴传感技术的新发展[J].微纳电子技术,2015,52(2):69-74Zhao Zhengping.The new development of typical MEMS and wearable sensing technology[J].Micronanoelectronic technology,2015,52(2):69-74.
[12]秦永元,张洪钺,汪叔华.卡尔曼滤波与组合导航原理[M].西安:西北工业大学出版社,2015.Qin Yongyuan,Zhang Hongjun,Wang Shuhua.Principles of Kalman Filtering and Integrated Navigation[M].Xi'an:Northwestern Polytechnical University Press,2015.