基于最大后验概率密度的粒子过滤器跟踪算法.pdfVIP

32 11 Vol.32, No.11 2005 11 Opto-Electronic Engineering Nov, 2005 1003501X(2005 310027 Kalman MAP 7% V556,TN713 A Particle filter tracking algorithm based on maximum a posteriori LIU Tian-jianZHU Shan-an (College of Electrical Engineering of Zhejiang University, Hangzhou 310027, China) Abstract The weakness of Kalman filter is that it is inadequate to solve the problem of non-linear and non-Gaussian model. A new model with importance weights, density assisted particle filter algorithm is proposed. This method represents the distribution of the states by weighted samples and iterates the change of states. The virtue of this algorithm is that it can adapt complex interactions with overlap and ambiguities and track to multiple objects simultaneously. Maximum A Posteriori (MAP) mode is exploited the statistical dependence between objects to provide accurate decisions and state estimation. Analysis of resample techniques reduces the sensitivity of the algorithm to noises. Weight correspondence to sample should be arranged in a distribution around the most possible area of object. The algorithm is implemented on the system of eye tracking. Simulation results suggest that MAP mode can potentially provide 7% improvement in accuracy over conventional mode. Results of eye tracking support the simulation studies. Key w