Prior Kinematic Information Fusion for Pedestrian Localization With Toe-Heel-Shank MIMUs

With the development of the microelectronic mechanical system (MEMS) inertial measurement units (IMUs), the foot-mounted pedestrian inertial navigation system (PINS) based on the zero velocity update is widely used. The systematic errors of MEMS IMU cause low precision of PINS at the swing phase. However, the biomechanical constraints, which can be used to suppress the divergence of PINS, exist in lower limbs. In this study, three magnetometers and IMUs are fixed on the toe, heel, and shank, respectively. The attitude of each segment is estimated by a MAHONY filter. The gait is defined as four successive phases. For each phase, the velocity and position constraints are constructed based on dynamic geometric constraint and Coriolis theory. Based on the perfect measurement method, the kinematic constraints, which are calculated by inertial data and attitude, are added into the observed equations. The noises are considered additive Gaussian white noise. The linear Kalman filter is set to iteratively estimate the optimal positioning result. Several experiments with different walking velocities and trajectories are performed. The results show that the algorithm, which is proposed in this study, greatly suppresses the velocity and positioning error.