Multi-target tracking can be divided into two scenarios: known number of targets and unknown number of targets. The latter integrates the birth and death processes of targets compared to the former, with the number of targets dynamically changing and the data association becoming more complex. In practical engineering applications, the number of targets is often unpredictable and dynamically changing, so this paper mainly studies multi-target tracking in the case of an unknown number of targets. In traditional tracking algorithms, MHT integrates the birth and death of targets through multiple hypotheses and theoretically has the best tracking performance, but its computational complexity grows exponentially with the increase in the number of targets and observations. To improve the performance and computational complexity of MHT, many improved algorithms have been developed, among which the Rao-Blackwellized Monte Carlo Data Association (RBMCDA) algorithm is a representative one.