Probabilistic Data Association Algorithm Based on Modified Input Estimation

For the problem of maneuvering target tracking in clutter, an adaptive probabilistic data association algorithm is proposed based on modified input estimation (MIE). An adaptive weighted factor is introduced in MIE in order to adjust the prediction covariance and the corresponding filter gain in real time, which enables the adaptive MIE (AMIE) method track a maneuvering target well. Then, the AMIE method is combined with the probabilistic data association (PDA) technique so that the proposed method can track a maneuvering target in clutter. Simulation results show the effectiveness of the proposed algorithm. Compared with the traditional algorithm, the proposed algorithm has better tracking performance.