Uncorrelated optical space observation association represents a classic needle in a haystack problem. The objective being to find small groups of observations that are likely of the same resident space objects (RSOs) from amongst the much larger population of all uncorrelated observations. These observations being potentially widely disparate both temporally and with respect to the observing sensor position. By training on a large representative data set this paper shows that a deep learning enabled learned model with no encoded knowledge of physics or orbital mechanics can learn a model for identifying observations of common objects. When presented with balanced input sets of 50% matching observation pairs the learned model was able to correctly identify if the observation pairs were of the same RSO 83.1% of the time. The resulting learned model is then used in conjunction with a search algorithm on an unbalanced demonstration set of 1,000 disparate simulated uncorrelated observations and is shown to be able to successfully identify true three observation sets representing 111 out of 142 objects in the population. With most objects being identified in multiple three observation triplets. This is accomplished while only exploring 0.06% of the search space of 1.66e8 possible unique triplet combinations.

中文翻译：

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深度学习启用不相关空间观测协会

不相关的光学空间观测关联代表了大海捞针问题。目标是从所有不相关观测的更大群体中找到可能属于相同驻地空间物体 (RSO) 的小组观测。这些观察可能在时间上和关于观察传感器位置都有很大差异。通过对大型代表性数据集的训练，本文表明，一个没有物理或轨道力学编码知识的深度学习学习模型可以学习识别常见物体观察的模型。当呈现 50% 匹配观测对的平衡输入集时，学习模型能够在 83.1% 的时间内正确识别观测对是否属于相同的 RSO。然后将得到的学习模型与搜索算法结合使用，用于 1,000 个不同的模拟不相关观察的不平衡演示集，并且显示能够成功识别代表总体中 142 个对象中的 111 个的真实三个观察集。大多数物体在多个三个观察三元组中被识别。这是在仅探索 1.66e8 个可能的唯一三元组组合的搜索空间的 0.06% 的同时实现的。