Infrared small target detection and tracking algorithm based on new closed-loop control particle filter

Abstract

The problem of particle impoverishment could be always found in standard particle filter, additionally a large number of particles are required for accurate estimation. as it is difficult to meet the demand of modern infrared search and tracking system. To solve this problem, an improved infrared small target detection and tracking method based on closed-loop control bat algorithm optimized particle filter is proposed. Firstly, bat algorithm is introduced into the particle filtering in this method. Particles are used to simulate the process that an individual bat hunts and avoids obstacles so that particles move towards the high-likelihood region. Meanwhile, the improved algorithm takes the proportion of particles accepting a new state as the feedback quantity and proposes to conduct dynamic control on global and local search ability of particle filtering by closed-loop control strategy, which further improves the overall quality of particle distribution. The performance of the improved detection and tracking algorithm is tested in simulation scene and real scene of infrared small target. Experimental results show that the improved algorithm improves the performance of the infrared searching and tracking system.