For the purpose of positioning in various scenes, including indoors, on open road, and side street buildings, a low-cost personal navigation unit is put forward. The unit consists of a low-cost MEMS(micro-electro-mech...For the purpose of positioning in various scenes, including indoors, on open road, and side street buildings, a low-cost personal navigation unit is put forward. The unit consists of a low-cost MEMS(micro-electro-mechanical system) accelerometer, a gyroscope, a magnetometer and a GPS(global positioning system) chip, and it is capable of switching modes between indoor and outdoor situations seamlessly. The outdoor mode is MIMU(MEMS-inertial measurement unit)/GPS/magnetometer integrated mode and the indoor mode is MIMU/magnetometer integrated mode. The outdoor algorithm uses the extended Kalman filter to fuse data and provide optimum parameters. The indoor algorithm is dead reckoning, which uses vertical and forward accelerations to judge steps and uses a magnetometer to define heading. The two-axis acceleration data is used to calculate the adaptive threshold and estimate the confidence value of the steps, and when the confidence of both two axes data meet the conditions, the steps can be detected in the adaptive time windows. The detection precision is more than 95%. An experiment was conducted in complex situations. The experiment participant wearing the unit walked about 1 600 m in the experiment. The results show that the positioning error is less than 0.2% of the total route distance.展开更多
基金The National Natural Science Foundation of China(No.61773113)International Special Projects for Scientific and Technological Cooperation(No.2014DFR80750)the National Key Research and Development Program of China(No.2016YFC0303006,2017YFC0601601)
文摘For the purpose of positioning in various scenes, including indoors, on open road, and side street buildings, a low-cost personal navigation unit is put forward. The unit consists of a low-cost MEMS(micro-electro-mechanical system) accelerometer, a gyroscope, a magnetometer and a GPS(global positioning system) chip, and it is capable of switching modes between indoor and outdoor situations seamlessly. The outdoor mode is MIMU(MEMS-inertial measurement unit)/GPS/magnetometer integrated mode and the indoor mode is MIMU/magnetometer integrated mode. The outdoor algorithm uses the extended Kalman filter to fuse data and provide optimum parameters. The indoor algorithm is dead reckoning, which uses vertical and forward accelerations to judge steps and uses a magnetometer to define heading. The two-axis acceleration data is used to calculate the adaptive threshold and estimate the confidence value of the steps, and when the confidence of both two axes data meet the conditions, the steps can be detected in the adaptive time windows. The detection precision is more than 95%. An experiment was conducted in complex situations. The experiment participant wearing the unit walked about 1 600 m in the experiment. The results show that the positioning error is less than 0.2% of the total route distance.
