Research on Improved Particle Filtering Algorithm for Targets Tracking in Passive Millimeter Wave Imaging

Abstract

Particle filtering has been proved effective for the state estimation of nonlinear and non-Gaussian systems. To solve the problems of sample degradation and depletion in standard particle filtering tracking algorithm, a novel immune particle filtering target tracking method is proposed in Passive Millimeter Wave (PMMW) imaging. Particle filtering provides a framework in which the posterior density of PMMW target state is represented by a weighted sample set. By using the artificial immune algorithm combined with the Mean Shift algorithm, the samples are optimized during the evolution process. To achieve robust description of the PMMW targets, both gray and gradient orientation distributions are taken into account. Besides, the observation density is established by computing the Bhattacharyya distance between the distribution of the target model and that of the candidate. Experimental results demonstrate that the proposed algorithm is superior to traditional ones when tracking scale changing PMMW targets.