Particle Filtering Algorithm And Its Application In Target Tracking

Target tracking is an important element of surveillance, guidance, or obstacle avoidance system, whose role is to determine the number, position, movement, and identity of targets. The fundamental building block of a tracking system is filter for recursive target state estimation.. Kalman Filter and Extended Kalman Filter are the most typical filter algorithms in target tracking domain, of which the former is adaptive to linear system and the latter is used in nonlinear system. Kalman Filter is an optimal filter algorithm in the Minimum-Mean-Square-Error sense, meanwhile Extended Kalman Filter is a sub-optimal filter algorithm, which derived from the linearization of nonlinear system using Taylor expansion. While the non-linearity of the system is not extreme strong, EKF can achieve approximately optimal filter effect. Although the above two methods own pretty good filtering performance when system noise and observation noise are non-Gaussian, their filtering performance will descend or even diverge when non-Gaussian distribution occurs. Thus, people begin to pay attention to filtering algorithm under nonlinear non-Gaussian background.Particle filter realize recursive Bayesian filter via Monte Carlo simulation. The method is suitable for any non-linear system that could be represented with state model. It is more practical than conventional Kalman filter and its precision could approach optimal estimation. Particle filter is flexible and easy to be implemented. And it is also has parallel structure.Particle filter algorithm is main content of this paper, which can be implemented by a Bayesian recursion process though a Monte Carlo simulation method. we discuss the algorithm of Particle Filter, analyze the advantage and disadvantage of this method. Eventually we make a simulation based on a non-linear background compared with the EKF algorithm and another simulation based on target tracking in order to present the advantage of the particle filter.