A Two-stage Transition Correction Function for Adaptive Markov Matrix in IMM Algorithm
In Ho Lee, Chan Gook Park · 2022 25th International Conference on Information Fusion (FUSION) · 2022
This paper proposes two probability correction functions to make adaptively the transition probability matrix(TPM). In a traditional interacting multiple model(IMM) estimator, TPM is usually considered a constant as initial values, so it is conveniently calculated by fixing prior information. However, inaccurate TPM can result in a large target state estimation error. To solve the problem, The IMM algorithm needs to have a time-varying transition probability so that the system model changes promptly according to the target movement. Therefore, a two-stage correction function is designed according to the period. The first phase is the accumulating transition probability correction function which increases the probability of the model matching the target movement and decreases others when the model jump does not occur. The second phase is the activating transition probability correction function which quickly updates the probability when the model jump occurs. By the performance comparison between the proposed adaptive IMM and the traditional IMM, the effect of probability correction functions is confirmed and the performance is improved.