# Double Deep Q Networks for Sensor Management in Space Situational Awareness

arXiv: https://arxiv.org/abs/2205.14041 · HTML: https://mlsanity.com/paper/2205.14041/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [Generalized optimal sub-pattern assignment metric](https://mlsanity.com/paper/1601.05585/) (2016)
- [An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA](https://mlsanity.com/paper/2110.11788/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Simulation-Augmented Benchmarking Framework for Automatic RSO Streak Detection in Single-Frame Space Images](https://mlsanity.com/paper/2305.00412/) (2023)
- [VISTA: An Attention-Based Multi-Agent Reinforcement Learning Architecture for Space Situational Awareness Sensor Tasking](https://mlsanity.com/paper/2609.23875/) (2026)

## Abstract (arXiv)

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.