存在非高斯重尾分布噪声的纯方位目标跟踪算法

The bearings-only target tracking is a classic problem in the reserach on target tracking. Focusing on the problem of non-Gaussian heavy-tailed distributed noise in the model of target tracking, this paper proposes a new Kalman filter algorithm. Firstly, the hierarchical Gaussian model is established to approximate the unknown process noise and measurement noise of the non-Gaussian heavy-tailed distributed system. Next, the variational Bayesian inference is used to learn Mixture Probability to solve the problem of the filter's performance degradation caused by the uncertainty of Mixture Probability, so as to improve the robustness of the filter. At the same time, for the nonlinearity of the bearings-only target tracking model, Modified Gain Kalman filter is used to reduce the influence of nonlinearity on the observation equation. The numerical simulations have verified that the proposed filter has better estimation accuracy and robustness than EKF, UKF and the variational Bayesian Kalman filters PEKF-Vb and VBEKF. The estimation accuracy of the proposed algorithm VBMGEKF is improved by 69. 31%, 58. 08%, 127. 84% and 9. 36% .