GMSPPF for Magnetic Ellipsoid Tracking with a Magnetic Gradiometer Tensor

Abstract

Magnetic ellipsoid tracking problem is characterized by high nonlinearity. In this study, the determination of target position, magnetic moment, and velocity is formulated as a Bayesian estimation problem for dynamic systems, a recursive approach is proposed to estimate the trajectory and magnetic moment component of the target using data collected with a magnetic gradiometer tensor. Particle filter provides a solution to this problem. In addition to the conventional particle filter, the proposed tracking and classification algorithm uses Gaussian mixed mode to represent the posterior state density of the unknown parameters, which is named as Gaussian mixture sigma point particle filters(GMSPPF). The performance of the proposed method has been evaluated through simulation experiment. The results indicate that the method has achieved the magnetic ellipsoid tracking and GMSPPF has better estimation performance and less computational complexity than other related algorithms.