Foot-mounted pedestrian navigation system(PNS)is a common solution to pedestrian navigation using micro-electro mechanical system(MEMS)inertial sensors.The inherent problems of inertial navigation system(INS)by the tr...Foot-mounted pedestrian navigation system(PNS)is a common solution to pedestrian navigation using micro-electro mechanical system(MEMS)inertial sensors.The inherent problems of inertial navigation system(INS)by the traditional algorithm,such as the accumulated errors and the lack of observation of heading and altitude information,have become obstacles to the application and development of the PNS.In this paper,we introduce a heuristic heading constraint method.First of all,according to the movement characteristics of human gait,we use the generalized likelihood ratio test(GLRT)detector and introduce a time threshold to classify the human gait,so that we can effectively identify the stationary state of the foot.In addition,based on zero velocity update(ZUPT)and zero angular rate update(ZARU),the cumulative error of the inertial measurement unit(IMU)is limited and corrected,and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian.After simulation and experiments with low-cost IMU,the method is proved to reduce the localization error of end-point to less than 1%of the total distance,and it has great value in application.展开更多
基金This work was supported by the National Natural Science Foundation of China(61803278).
文摘Foot-mounted pedestrian navigation system(PNS)is a common solution to pedestrian navigation using micro-electro mechanical system(MEMS)inertial sensors.The inherent problems of inertial navigation system(INS)by the traditional algorithm,such as the accumulated errors and the lack of observation of heading and altitude information,have become obstacles to the application and development of the PNS.In this paper,we introduce a heuristic heading constraint method.First of all,according to the movement characteristics of human gait,we use the generalized likelihood ratio test(GLRT)detector and introduce a time threshold to classify the human gait,so that we can effectively identify the stationary state of the foot.In addition,based on zero velocity update(ZUPT)and zero angular rate update(ZARU),the cumulative error of the inertial measurement unit(IMU)is limited and corrected,and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian.After simulation and experiments with low-cost IMU,the method is proved to reduce the localization error of end-point to less than 1%of the total distance,and it has great value in application.
