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首先针对无源传感器目标跟踪中的非线性问题,将高斯-厄米特求积分规则运用于高斯混合概率假设密度滤波,提出一种求积分卡尔曼概率假设密度滤波。其次,针对未知时变过程噪声,将基于极大后验估计原理的噪声估计器运用到概率假设密度滤波中,同时依据目标状态一步预测与状态滤波结果之间的残差,提出一种对滤波发散情况判断和抑制的算法。最后通过无源传感器双站跟踪仿真表明:相较于已有的非线性高斯混合概率假设密度滤波,所提算法有更高的精度,并且在未知时变噪声环境中具有较好跟踪效果。
Firstly, a Gaussian-Hermitian integral rule is applied to the Gaussian mixture probability hypothesis density filter to solve the nonlinear problem in passive sensor target tracking. An integral Kalman probability hypothesis density filter is proposed. Secondly, for the unknown time-varying process noise, the noise estimator based on the principle of maximum a posteriori estimation is applied to the probability hypothesis density filtering. At the same time, based on the one-step prediction of the target state and the residuals between the state filtering results, Divergence judgment and suppression algorithm. Finally, the simulation results of two-station passive sensor tracking show that compared with the existing non-linear Gaussian mixture probability hypothesis density filtering, the proposed algorithm has higher accuracy and better tracking performance in the unknown time-varying noise environment.