论文标题
深度学习启用了不相关的太空观察协会
Deep Learning Enabled Uncorrelated Space Observation Association
论文作者
论文摘要
不相关的光学空间观察协会代表了干草堆问题中的经典针头。目的是从所有不相关的观测值中的人群中找到可能是相同居民空间对象（RSO）的一小组观测值。这些观察结果可能在时间和观测传感器位置方面存在于时间和时间上。通过对大型代表性数据集进行培训，本文表明，没有编码物理学或轨道力学知识的深度学习的学习模型可以学习一个模型，以识别公共对象的观察结果。当呈现50％匹配的观察对的平衡输入集时，学到的模型能够正确识别观测对是否在同一时间为83.1％。然后，在不平衡的1,000个不同模拟无关的观察结果的不平衡演示集上，将所得的学到的模型与搜索算法结合使用，并被证明能够成功识别代表人群中142个对象中111个对象的真实三个观察集。大多数对象都在多个三个观察三联体中识别。这是在仅探索1.66E8可能独特三重组合组合的0.06％的0.06％的同时完成的。
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.
