Double Deep Q Networks for Sensor Management in Space Situational Awareness
This work addresses the problem of efficiently managing limited sensors for tracking increasing satellites in space, but it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the sensor management challenge in space situational awareness by applying a Double Deep Q Network to maximize satellite tracking with a simulated telescope, resulting in greatly reduced state covariance matrices compared to a random policy.
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.