An improved multi-targets tracking algorithm based on cubature information particle filter

In the traditional bootstrap particle filter, the state transition density is used as the importance sampling function, which brings some problems such as particle degradation and poor tracking accuracy. In this paper, the posterior probability is used as the importance sampling function and its estimation method is proposed. By means of cubature information filtering and Gating technique, the mean and variance of the importance sampling function are estimated, and the importance sampling function is designed. The improved particle filter method is used to estimate the number of targets and the number of targets in the nonlinear situation. The simulation results show that the proposed algorithm has the advantages of high estimation accuracy and good stability in the nonlinear multi-target tracking scenario.