Tracking application about singer model based on marginalized particle filter

This article deals with the problem of maneuvering target tracking which results in a mixed linear/non-linear model estimation problem. For maneuvering tracking system, extended Kalman filter (EKF) or particle filter (PF) is traditionally used to estimate the states. In this article, marginalized particle filter (MPF) is presented for application in a mixed linear/non-linear model estimation problem. MPF is a combination of Kalman filter (KF) and PF. So it holds both advantage of them and can be used for mixed linear/non-linear substructure, where the conditionally linear states are estimated using KF and the nonlinear states are estimated using PF. Simulation results show that MPF guarantees the estimation accuracy and alleviates the potential computational burden problem compared with PF and EKF in maneuvering target tracking application.