We present a novel Double Deep Q Network (DDQN) application to a sensor management problem in space situational awareness (SSA). Frequent launches of satellites into Earth orbit pose a significant sensor management challenge, whereby a limited number of sensors are required to detect and track an increasing number of objects. In this paper, we demonstrate the use of reinforcement learning to develop a sensor management policy for SSA. We simulate a controllable Earth-based telescope, which is trained to maximise the number of satellites tracked using an extended Kalman filter. The estimated state covariance matrices for satellites observed under the DDQN policy are greatly reduced compared to those generated by an alternate (random) policy. This work provides the basis for further advancements and motivates the use of reinforcement learning for SSA.

翻译：我们对空间情境意识中的传感器管理问题提出了一种新颖的双深Q网络(DDQN)应用。卫星经常发射到地球轨道构成一个重大的传感器管理挑战,因此需要数量有限的传感器来探测和跟踪越来越多的物体。在本文件中,我们展示了利用强化学习来制定SSA传感器管理政策的情况。我们模拟了可控的地球望远镜,该望远镜经过培训,以最大限度地增加使用扩展的Kalman过滤器跟踪的卫星数量。与替代(随机)政策相比,根据DDQN政策观测的卫星的估计状态共变矩阵大大减少。这项工作为在SSA方面进一步推进和鼓励使用强化学习奠定了基础。