Target Tracking Nonlinear Filtering Algorithms

The target tracking technique has been applied in different kinds of martial and civil areas.It is one of the scientific topics which draw lots of research interests nowadays.The key of target tracking is filtering algorithm.It is a hotspot and nodus in target tracking technique research to propose more reliable non linear filtering algorithms to cope with the non linear and non Gaussian problem,and applied in practical tracking system efficiently.Firstly,for single target tracking system,a novel mixed filtering algorithm is proposed for mixed linear/nonlinear state space models.The algorithm utilizes the idea of Rao-Blackwellized to separate the linear and nonlinear states.For the nonlinear states,the posterior distributions of the estimates,which are achieved by the quasi-Gaussian particle filter,are approximated as Gaussian distributions.Also,the linear states are estimated by the Kalman filter with the estimated nonlinear states. The simulation results show that the proposed method saves much computing time with no declined tracking accuracy performance.Next,we proposed a novel random set based filtering algorithm for multi-target tracking(MTT) application.The algorithm,while utilizing the idea of Rao-Blackwell to enhance the estimating performance of the probability hypothesis density(PHD), adopts the sequential Monte Carlo(SMC) method to predict and estimate the nonlinear states of the multiple targets.In addition,the linear states are estimated by the Kalman filter(KF) with the information embedded in the estimated nonlinear states.Simulation results of the proposed method show that,in addition to reducing particle dimensions and computation complexity,the proposed method significantly enhances tracking accuracyMoreover,another novel probability hypothesis density filtering algorithm is proposed for multi-target tracking applications.The algorithm utilizes the kernel density estimation theory and the mean-shift algorithm to further estimate the probability hypothesis density and then to extract target state estimates after the computation of the PHD recursive formula.The simulation results of the proposed method show that,the tracking accuracy of the proposed method is increased significantly.