Ford, SA;
Ritchie, M;
(2022)
Cognitive radar mode control: a comparison of different reinforcement learning algorithms.
In:
International Conference on Radar Systems (RADAR 2022).
(pp. pp. 107-112).
Institution of Engineering and Technology: Hybrid Conference, Edinburgh, UK.
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Abstract
This paper describes the use of deep reinforcement learning (RL) to apply the concept of cognition in sensing systems to the choice of operational radio frequency (RF) mode (active, bistatic receive, electronic surveillance (ES), electronic protection measure (EPM)) for a multi-function RF system (MFRFS). This is investigated in a simulated air-to-air combat scenario, with the RL on a blue fast jet rewarded for successfully guiding a missile to the opposition, a red fast jet, and penalised if the red jet is successful. Three RL algorithms (deep Q-network (DQN), advantage actor-critic (A2C), and proximal policy optimisation (PPO)) are compared with baselines that include the 4 static modes and a set of fixed rulesets, and it is shown that - after hyperparameter tuning - the algorithms perform comparably to these baselines. It is suggested that PPO might be the optimal algorithm in this context.
| Type: | Proceedings paper |
|---|---|
| Title: | Cognitive radar mode control: a comparison of different reinforcement learning algorithms |
| Event: | International Conference on Radar Systems (RADAR 2022) |
| ISBN-13: | 9781839538391 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1049/icp.2022.2300 |
| Publisher version: | https://doi.org/10.1049/icp.2022.2300 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | cognitive radar, optimisation, radar computing, reinforcement learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10180994 |
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