A modified marginalized Kalman filter for maneuvering target tracking

This paper uses a new nonlinear Kalman filter algorithm in maneuvering target tracking. Marginalized KalmanFilter (MKF) algorithm expresses the system´s nonlinear measurement equation as weighted sum of Hermite polynomials, then models the prior distribution of the weighted matrix as a Gaussian process. After that, calculates the weighted matrix´s posteriori distribution and removes the influence of the weighted matrix by calculating its integration. As a result, the closed-form of the system´s state and its covariance are available. To improve the stability of the MKF algorithm, Strong Tracking Filter concept is brought in by using a fading factor in the MKF algorithm. It can decrease the influence of previous filtering step on the current step. The Strong Tracking Marginalized Kaiman Filter (STMKF) algorithm has a adaptability to the sudden maneuver of the target. Using STMKF with UKF, CKF, and standard MKF in high maneuvering target tracking problem, the result shows that the STMKF has a better stability and a higher accuracy.