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

Double Deep Q Networks for Sensor Management in Space Situational Awareness