The IMM tracking algorithm for maneuvering target with adaptive Markov transition probability matrix

This paper proposes an interacting multi-model (IMM) tracking algorithm based on the adaptive Markov transition probability matrix, which can be utilized in radar systems for maneuvering target tracking. The algorithm constructs likelihood ratio function of motion model, and presents an adaptive Markov transition probabilities calculated thought. Base on “new information” structure model of motion of the likelihood function, tracking system can online adjustment model of the noise variance and the Markov matrix adaptively. The Monte Carlo simulation is carried out by software, which shows that the tracking performance of the algorithm is superior to the traditional method of IMM.