When building a space catalogue, it is necessary to acquire multiple observations of the same object for the estimated state to be considered meaningful. A first concern is then to establish whether different sets of observations belong to the same object, which is the association problem. Due to illumination constraints and adopted observation strategies, small objects may be detected on short arcs, which contain little information about the curvature of the orbit. Thus, a single detection is usually of little value in determining the orbital state due to the very large associated uncertainty. In this work, we propose a method that both recognizes associated observations and sequentially reduces the solution uncertainty when two or more sets of observations are associated. The six-dimensional (6D) association problem is addressed as a cascade of 2D and 4D optimization problems. The performance of the algorithm is assessed using objects in geostationary Earth orbit, with observations spread over short arcs.

中文翻译：

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在短弧上确定的轨迹的数据关联和不确定性修剪

在构建空间目录时，需要对同一对象进行多次观测，以使估计的状态被认为是有意义的。然后首先关注的是确定不同的观察集是否属于同一对象，这就是关联问题。由于光照限制和所采用的观测策略，小物体可能会在短弧上被检测到，其中包含的轨道曲率信息很少。因此，由于非常大的相关不确定性，单次探测在确定轨道状态方面通常没有什么价值。在这项工作中，我们提出了一种方法，该方法既可以识别相关观察，又可以在两组或多组观察相关时依次降低解的不确定性。六维 (6D) 关联问题作为 2D 和 4D 优化问题的级联解决。该算法的性能是使用地球静止轨道上的物体评估的，观测分布在短弧上。