GREY PREDICTION BASED PARTICLE FILTER.pdf

Progress In Electromagnetics Research, PIER 93, 237–254, 2009 GREY PREDICTION BASED PARTICLE FILTER FOR MANEUVERING TARGET TRACKING J.-F. Chen, Z.-G. Shi, S.-H. Hong, and K.-S. Chen Department of Information and Electronic Engineering Zhejiang University Hangzhou 310027, China Abstract—For maneuvering target tracking, we propose a novel grey prediction based particle filter (GP-PF), which incorporates the grey prediction algorithm into the standard particle filter (SPF). The basic idea of the GP-PF is that new particles are sampled by both the state transition prior and the grey prediction algorithm. Since the grey prediction algorithm is a kind of model-free method and is able to predict the system state based on historical measurements other than establishing a priori dynamic model, the GP-PF can significantly alleviate the sample degeneracy problem which is common in SPF, especially when it is used for maneuvering target tracking. Simulations are conducted in the context of two typical maneuvering motion scenarios and the results indicate that the overall performance of the proposed GP-PF is better than the SPF and the multiple model particle filter (MMPF) when the tracking accuracy, computational complexity and tracking lost probability are considered. The performance improvements can be attributed to that the GP-PF has both model-based and model-free features. 1. INTRODUCTION The problem of target tracking has been an important issue of signal processing for many years, and a variety of tracking methods have been proposed in literatures [1–4]. For linear Gaussian problems, the Kalman Filter (KF) can be applied to obtain optimal solutions [5– 7]. For nonlinear problems, many nonlinear filtering techniques have been proposed, such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which are usually implemented to provide Corresponding author: Z.-G. Shi (shizg@zju.edu.cn). 238 Chen et al. Gaussian approximation to the posterior probability density