三维数据关联情况下外辐射源雷达多目标跟踪研究

不同于传统多目标跟踪，除了量测-目标数据关联模糊问题外，外辐射源雷达跟踪系统新增了量测-发射机数据关联模糊问题。针对此问题，该文通过引入一个新的关联变量来表示量测和发射机之间的数据关联关系，提出了目标-量测-发射机3维数据关联改进概率多假设跟踪(PMHT)算法。该算法利用期望极大化(EM)算法的独立性假设条件得到最大后验概率意义下的最优跟踪。为了增加目标-量测-发射机之间数据关联的准确性，提高多目标与量测后验关联概率的精确度，将量测信息设定为均值相同协方差不同的混合高斯分布。针对距离-多普勒量测的非线性性，利用无味卡尔曼平滑(UKS)算法进行多目标状态估计。仿真结果表明，对于FKIE外辐射源雷达数据集(杂波密度很高)，所提算法的目标与航迹关联成功率高，抗杂波性能强，证明了算法的有效性。

Multistatic Passive Radar Multi-target Tracking Under Target-measurement-illuminator Data Association Uncertainty

Different from the traditional multi-target tracking problem which has the measurements to targets data association uncertainty problem, the multistatic passive radar multi-target tacking system has the additional measurements to illuminators data association uncertainty problem, which means the data association relationship is three dimensional. A novel target-measurement-illuminator Probabilistic Multiple Hypothesis Tracking (PMHT) algorithm is proposed, which introduces a new data association variable to represent the data association relationship. The proposed algorithm is based on the Expectation-Maximization (EM). To handle the nonlinear problem of range-Doppler measurements, the Unscented Kalman Smoother (UKS) is used to get the multi-targets’ estimated states. To increase the data association accuracy, the measurements are set to mixture Gaussian distribution. Simulation results show that for the FKIE passive radar data set, the proposed algorithm can track multi-targets effectively in dense clutter environment.