Abstract:

A new interacting multiple model algorithm based on the convolution particle filter is proposed for nonlinear target tracking when the distribution of noise is unknown. The algorithm utilizes the convolution particle filter to run multiple models in parallel. The previous state posterior probabilities of all models interact each other. Samples from the interacted probability density are regarded as the current initial particles. The outputs of all parallel filters are weighted as system outputs. Compared with the interacting multiple model algorithm based on particle filter (IMMPF), the new algorithm improves the effectivenesscost ratio and eliminates the correlation between the algorithm and analytical probability distribution of measurement noises. The theoretical analysis and simulation results show the effectiveness of the proposed algorithm.