在弹道导弹防御系统中，群目标跟踪是目前较为困难的问题之一。这些目标不仅具有相似的运动特性，且相互邻近，又由于红外光学探测器的特性和分辨率的影响，使得它们在像平面不再是点目标而是簇状像斑。因此，“一个目标至多产生一个量测”的传统多目标跟踪方法不再适用。为了实现对该类目标的有效跟踪，提出了一种新型滤波算法。该算法视群目标为一个整体，用椭圆随机超曲面模型描述其扩散程度，并将其与扩展目标高斯混合概率假设密度(PHD)滤波器相结合，通过跟踪群质心和扩散程度实现对像平面群目标的跟踪。通过仿真对比，所提算法在质心状态和扩散程度的估计精度方面均明显优于基于随机矩阵的高斯逆韦氏分布的概率假设密度滤波器。

Tracking group targets is one of the major challenges in modern ballistic missile defense system. These targets not only move in an analogue pattern but also are adjacent in space. The projections of group targets on the focal plane array are no longer points but clusters instead, according to the characterization and resolution of the infrared optic sensors. Thus the traditional multi-target tracking methods based on the assumption that each target generates at most one measurement are not fitted any more. In order to realize the group targets tracking, a new filter is proposed. The group targets are treated as a union and the extension of the group is described by an ellipse random hyersurface model. Combined with the Gaussian mixture probability hypothesis density (PHD) filter for extended targets, the group targets are tracked by its centroid states and extensions. With comparisons of the Gaussian inverse Wishart PHD based on the random matrix, the proposed method outperforms the latter one in extension estimation as well as centroid state estimation.