基于自适应马尔科夫矩阵的IMMPF目标跟踪

his paper uses adaptive markov transition matrix based on the a posteriori probability to study a kind of target tracking algorithm with particle filter as the core. At the end of each filter iteration process, we study two methods to update Markov transition matrix for the next iteration process. One is with the ratio of likelihood function, and the other is with the compress ratio of estimation error. Furthermore, to avoid possible failure resulted from abnormal data during the iteration process; we set the upper bound to constrain Markov transition probability. Simulations show that constrained adaptive Markov transition matrix is beneficial to improve interacting multiple models particle filter results.