Identification of known space objects is a critical step in maintaining accurate catalogs for space situational awareness activities. With increasing numbers of objects in orbit, optical measurements of space objects become more populated with detections, stressing the algorithms used to track and identify these objects. Traditional algorithms used for identifying space objects, such as elliptical gating, suffer from ambiguous or incorrect classifications as gates tend to overlap in dense detection environments. An algorithm is developed that couples elliptical gating with a star pattern recognition algorithm called the planar triangle method to overcome the difficulties found in spatially dense observations. Unlike star catalogs, cataloged resident space objects often contain considerable uncertainty, further challenging the identification of objects in a cluttered field-of-view. The proposed approach leverages uncertainties of the catalog as well as the optical measurement sensor uncertainty to support the space object identification. Simulation results using the gating-assisted planar triangle method show a significant improvement in robust identification of space objects as compared to traditional elliptical gating methods when faced with highly cluttered observations.

识别已知空间物体是维护空间态势感知活动的准确目录的关键步骤。随着在轨物体数量的增加，对空间物体的光学测量越来越多地出现在探测中，这给用于跟踪和识别这些物体的算法带来了压力。由于门倾向于在密集的检测环境中重叠，因此用于识别空间物体的传统算法（例如椭圆门控）存在模棱两可或不正确的分类。开发了一种将椭圆门控与称为平面三角形方法的星形模式识别算法相结合的算法，以克服在空间密集观测中发现的困难。与星表不同，编入目录的居民空间物体通常包含很大的不确定性，进一步挑战了在杂乱的视野中识别对象的能力。所提出的方法利用了目录的不确定性以及光学测量传感器的不确定性来支持空间物体识别。当面对高度混乱的观测结果时，与传统的椭圆选通方法相比，使用选通辅助平面三角形方法的仿真结果显示出对空间物体的鲁棒识别的显着改进。