Progressive Kalman Filter and Its Application in Magnetic Target Tracking
Suihua Zhou, Shan Shan, Hongxin Zhang, Zhonghua Dai · 2019 4th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) · 2019
A nonlinear filter is proposed for magnetic dipole tracking. Firstly, the state-space model of magnetic dipole target is established. Secondly, the Bayesian discrete-time update is reformulated to a continuous progressive Bayesian problem, and the analytical solution is derived under linear Gaussian condition. Further the first-order Taylor expansion is applied to obtain the nonlinear approximate solution expression, thus deriving the progressive extended Kalman filter (PEKF). Simulation and real-world magnetic dipole tracking experiments are performed to demonstrate the effectiveness of PEKF. Both the simulation and measured target tracking test results suggest that the progressive extended Kalman filter has good precision and convergence, which can effectively suppress the performance degeneration and filter divergence caused by large initial error in magnetic target tracking, and the computational efficiency is equivalent to that of extended Kalman filter, which is suitable for practical magnetic dipole tracking application.