Marginalized Particle Filter for Maneuvering Target Tracking Application

This paper deals with the problem of maneuvering target tracking in wireless tracking service. It results in a mixed linear/non-linear Models estimation problem. For maneuvering tracking systems, these problems are traditionally handled using the extended Kalman filter or Particle filter. In this paper, Marginalized Particle Filter is presented for applications in such problem. The algorithm marginalized the linear state variables out from the state space. The nonlinear state variables are estimated by the Particle Filter and the rest are estimated with the result of the estimation of the nonlinear state variables by the Kalman Filter. Simulation results shows that the Marginalized Particle Filter guarantees the estimation accuracy and reduces computational times compare to the Particle filer and the Extending Kalman Filter in maneuver target tracking application.