Double Deep Q Networks for Sensor Management in Space Situational Awareness

2022/05/27 by Benedict Oakes, Oakes, Benedict, Dominic Richards +5 · 1 citation
Engineering · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Satellite Communication Systems #Space Satellite Systems and Control #Space exploration and regulation

paper · pdf · doi:10.48550/arxiv.2205.14041

openalex publication_date 2022/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• GOSPA-Driven Non-Myopic Multi-Sensor Management with Multi-Bernoulli Filtering

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