Adaptive Markov transition matrix based multiple targets tracking for phased array radar

摘要

With the development of multifunctional radar and radio frequency (RF) stealth technology, modern radar needs to save as much operating time as possible. During the process of radar target tracking, with interacting multiple models (IMM), this paper proposes an adaptive Markov transition matrix to update the last step for existing radar target tracking algorithms. First, we take interacting multiple models as the main algorithm frame. Then, using gray relation and particle swarm optimization (PSO), multiple-target adaptive sampling interval algorithm is adopted. After the PSO process, we study two methods to update a Markov transition matrix in real time. One is with the ratio of likelihood function, and the other is with the compression ratio of estimation error. Simulations illustrate that our method is effective in reducing operating time for radars.