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, to solve the nonlinear problem in the field of passive tracking, Gauss-Hermite quadrature is used to Gaussian mixture probability hypothesis density filter, and the quadrature Kalman probability hypothesis density filter was proposed. Then under the condition of unknown and time-varying process noise statistic, a noise statistic estimator based on maximum a posterior estimation was used in probability hypothesis density filter. According to the residual between predicted state and estimated state, an algorithm to judge and restrain filter divergence was proposed. Finally, simulations under the condition that two passive sensors tracking multiple targets show that：the proposed algorithm has better accuracy than existing algorithms, and achieve good effect when process noise statistic is unknown and time-varying.