Strong Tracking Current Statistical Model Based on Mixed Bayesian-Fisher Model

The current statistical (CS) model has poor performance on tracking weak and sudden maneuvering targets due to non-adaptively adjusting the maneuvering parameters. To solve this problem, a new method based on mixed Bayesian-Fisher model is proposed. By introducing the mixed Bayesian-Fisher model and fusing input estimation (IE) theory, the maneuvering acceleration is adaptive estimated as an additional input term in the corresponding state equation. Then, a multiple fading factor is introduced to enhance the stability and robustness of the algorithm. The simulation results show that the strong tracking current statistical model based on mixed Bayesian-Fisher model (BF-STFCS) eliminates the dependence on initial maneuvering parameters, the tracking accuracy is significantly improved compared with the current statistical model, and the algorithm proposed performs even better than the interacting multiple model (IMMCVCACT). While the complexity of the proposed is only one-third of that of IMMCVCACT, slightly higher than the CS model.