Double Deep Q Networks for Sensor Management in Space Situational Awareness

Oakes, Benedict, Richards, Dominic, Barr, Jordi ORCID: 0000-0002-3037-2011 and Ralph, Jason ORCID: 0000-0002-4946-9948
(2022) Double Deep Q Networks for Sensor Management in Space Situational Awareness. In: 2022 25th International Conference on Information Fusion (FUSION), 2022-7-4 - 2022-7-7, Linköping, Sweden.

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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.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Reinforcement Learning, Sensor Management, Space Situational Awareness
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 27 May 2022 11:25
Last Modified: 18 Jan 2023 21:00
DOI: 10.23919/fusion49751.2022.9841242
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