The movement of the background caused by the movement of a sensor platform has always been a major challenge in space-based infrared weak and small target tracking. In addition to clutter, false alarms, noise, and other disturbances, the most serious source of interference in space-based infrared weak target tracking comes from the stars. This kind of interference always exists and is large enough to be often mistakenly recognized as a new target by traditional algorithms. To reduce the influence of sensor platform movement and stellar interference on the target tracking of weak points in space, this paper respectively proposes a fast iterative closest point (ICP) registration algorithm for sparse target points and a threshold separation clustering algorithm. After pruning and merging using a Gaussian mixture probability hypothesis density (GM-PHD) filter-based algorithm, the threshold separation cluster separates the weak point targets by thresholds, and then clusters according to the Euclidean distance between the targets. Moreover, to prevent stars with special positions from being clustered into a single group, a dynamic weight extraction scheme is adopted to better distinguish stars in the group. We compared the proposed algorithm with the original GM-PHD, forward-backward smoothing (FBS), and N-scan approaches in starry-sky tracking scenarios simulated using the Tycho-2 catalog. Experimental results show that the proposed algorithm has better tracking accuracy and a lower rate of false detections.

由传感器平台的移动引起的背景移动一直是天基红外弱小目标跟踪中的主要挑战。除了混乱，虚假警报，噪声和其他干扰之外，天基红外弱目标跟踪中最严重的干扰源还来自星星。这种干扰总是存在并且足够大，以至于传统算法经常被误认为是新目标。为了减少传感器平台运动和恒星干扰对空间弱点目标跟踪的影响，分别针对稀疏目标点提出了一种快速迭代最近点（ICP）配准算法和阈值分离聚类算法。使用基于高斯混合概率假设密度（GM-PHD）过滤器的算法进行修剪和合并后，阈值分离聚类通过阈值分离弱点目标，然后根据目标之间的欧几里得距离进行聚类。此外，为了防止具有特殊位置的恒星聚集到一个组中，采用动态权重提取方案以更好地区分该组中的恒星。我们将拟议的算法与原始GM-PHD，前向后平滑（FBS）和N-scan方法在使用Tycho-2目录模拟的星空跟踪场景中进行了比较。实验结果表明，该算法具有较好的跟踪精度和较低的误检率。阈值分离聚类通过阈值分离弱点目标，然后根据目标之间的欧式距离进行聚类。此外，为了防止具有特殊位置的恒星聚集到一个组中，采用动态权重提取方案以更好地区分该组中的恒星。我们将拟议的算法与原始GM-PHD，前向后平滑（FBS）和N-scan方法在使用Tycho-2目录模拟的星空跟踪场景中进行了比较。实验结果表明，该算法具有较好的跟踪精度和较低的误检率。阈值分离聚类通过阈值将弱点目标分开，然后根据目标之间的欧式距离进行聚类。此外，为了防止具有特殊位置的恒星聚集到一个组中，采用动态权重提取方案以更好地区分该组中的恒星。我们将拟议的算法与原始GM-PHD，前向后平滑（FBS）和N-scan方法在使用Tycho-2目录模拟的星空跟踪场景中进行了比较。实验结果表明，该算法具有较好的跟踪精度和较低的误检率。采用动态权重提取方案以更好地区分组中的恒星。我们将拟议的算法与原始GM-PHD，前向后平滑（FBS）和N-scan方法在使用Tycho-2目录模拟的星空跟踪场景中进行了比较。实验结果表明，该算法具有较好的跟踪精度和较低的误检率。采用动态权重提取方案以更好地区分组中的恒星。我们将拟议的算法与原始GM-PHD，前向后平滑（FBS）和N-scan方法在使用Tycho-2目录模拟的星空跟踪场景中进行了比较。实验结果表明，该算法具有较好的跟踪精度和较低的误检率。