论文标题
在空间情境意识中用于传感器管理的双重Q网络
Double Deep Q Networks for Sensor Management in Space Situational Awareness
论文作者
论文摘要
我们向空间情境意识（SSA）中的传感器管理问题提出了一种新颖的双重Q网络（DDQN）应用。卫星经常发射到地球轨道上构成了重要的传感器管理挑战，因此需要有限的传感器来检测和跟踪越来越多的物体。在本文中，我们证明了使用强化学习来制定SSA的传感器管理政策。我们模拟了可控的基于地球的望远镜，该望远镜经过训练，以最大程度地使用扩展的卡尔曼过滤器跟踪的卫星数量。与替代（随机）策略产生的卫星相比，根据DDQN政策观察到的卫星的估计国家协方差矩阵大大减少了。这项工作为进一步的进步提供了基础，并激发了SSA的增强学习。
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.
