Maneuvering Radar Targets Tracking with Kalman Particles Filter

The unscented particles filter has poor real-time performance and converges slowly in the beginning of radar maneuvering target tracking.The Kalman particle filter was used to solve the problem.Firstly,the radar measurements which were measured under polar coordinates were transformed into data of Cartesian coordinates.Secondly,the prior probabilistic density of the particles was obtained based on the linear optimal Kalman filters.Then,the posterior probabilistic density of targets' state was computed out using the nonlinear optimal particles.Compared with UPF,the time the KPF consumed was only about 1/5 of the UPF in tracking the radar maneuvering targets,and the cost of KPF was just about 6% of precision.Besides,in the beginning of tracking the KPF converged more quickly.