中文摘要：

雷达机动目标的无源定位跟踪是一个典型的非线性滤波问题，已有交互式多模型扩展卡尔曼滤波（IMM—EKF）和交互式多模型不敏卡尔曼滤波（IMM—UKF）应用其中，且IMM—UKF跟踪精度较高。但实际上目标还会受到非常复杂的非高斯噪声干扰，上述两种算法尚无法解决。针对雷达目标跟踪过程中的非高斯滤波的问题，研究一种雷达机动目标跟踪无源定位的优化算法，将交互式多模型粒子滤波（IMM—PF）算法应用于无源时差雷达定位系统中，即基于时差的IMM—PF无源定位算法，来跟踪雷达机动目标。经过在非高斯噪声环境下的仿真比较，优化算法具有较高的跟踪精度，验证了优化算法的有效性，对雷达机动目标定位跟踪问题具有一定的实际应用价值。

英文摘要：

Passive location and tracking for radar maneuvering target is a typical nonlinear filtering problem. IMM -EKF and IMM-UKF has been applied to it, and IMM-UKF has a higher tracking precision. But actually in the process of tracking, the target will subject to complicated non-Gaussian noise. The above two algorithms are difficult to solve non-Gaussian noise. Aimed at non-Gaussian filtering problem in the process of radar maneuvering target tracking, this paper presents an optimization algorithm of tracking and passive location of radar maneuvering target. IMM-PF is applied to the passive radar location system, namely IMM-PF algorithm for passive location based on TDOA, to track radar maneuvering target. The simulation under non-Gaussian environment shows that the optimiza- tion algorithm has higher tracking precision. The validity of the optimization algorithm is proved, and the optimization algorithm has some practical application value for radar maneuvering target location and tracking.