IMM estimator based on fuzzy weighted input estimation for tracking a maneuvering target

The application of target motion models and filters for interactive multiple model (IMM) estimator determines the effectiveness of maneuvering target tracking. In this paper, the fuzzy logic theory is utilized to construct the fuzzy weighting factor to improve the input estimation method and that is used to compute the unknown acceleration input for the modified Singer acceleration model. The proposed IMM estimator is operated mainly by two different target motion models combined with filters and the switch of target models is through the Markov transition probability matrix. The constant velocity model is combined with Kalman filter for the uniform target state estimation and the other one uses the modified Singer acceleration model to track the maneuvering target by the fuzzy weighted input estimation method. The performance of the proposed algorithm is verified by two different scenarios and compared with two IMM estimators. The target motion state of simulation condition contains the constant velocity, weak acceleration and strong acceleration. The simulation results show that the proposed IMM estimator has the better estimation precision in terms of tracking error. The modified Singer acceleration model combined with the fuzzy weighted input estimation method can track the maneuvering target effectively.