Interacting multiple model algorithm based on joint likelihood estimation

A novel approach is proposed for the estimation of likelihood on Interacting Multiple-Model (IMM) filter. In this approach, the actual innovation, based on a mismatched model, can be formulated as sum of the theoretical innovation based on a matched model and the distance between matched and mismatched models, whose probability distributions are known. The joint likelihood of innovation sequence can be estimated by convolution of the two known probability density functions. The likelihood of tracking models can be calculated by conditional probability formula. Compared with the conventional likelihood estimation method, the proposed method improves the estimation accuracy of likelihood and robustness of IMM, especially when maneuver occurs.