摘要: A shoe-mounted indoor positioning system is designed based on the low-cost MEMS (Micro-Electro-Mechanical System) inertial measurement unit (TMU). To solve the problem of the high noise and drift of the MEMS inertial sensor, the m... 展开 A shoe-mounted indoor positioning system is designed based on the low-cost MEMS (Micro-Electro-Mechanical System) inertial measurement unit (TMU). To solve the problem of the high noise and drift of the MEMS inertial sensor, the method of the coarse calibration for MIMU is studied. Aiming at solving the shortcomings of the traditional zero velocity interval detection algorithm with single threshold, an alternative algorithm for the zero velocity interval detection based on multiple gaits is designed, which judges the motion state before detection. Then, the Kalman filter algorithm based on the zero velocity update (ZUPT) and zero angular rate update (ZARU) is designed to estimate and compensate the cumulative error of the sensors during walking. Finally, a field test based on a low-cost MEMS IMU is carried out with four different gaits, slow walking, up and down staircase, striding forward and long-distance walking with variable speed. The results show that the positioning error of the proposed method is only 3% of the walking distance. 收起