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Reinforcement learning-based radar-evasive path planning: a comparative analysis

Published online by Cambridge University Press:  26 October 2021

R.U. Hameed Open the ORCID record for R.U. Hameed [Opens in a new window] ,
A. Maqsood Open the ORCID record for A. Maqsood [Opens in a new window] ,
A.J. Hashmi ,
M.T. Saeed Open the ORCID record for M.T. Saeed [Opens in a new window]  and
R. Riaz Open the ORCID record for R. Riaz [Opens in a new window]
R.U. Hameed
Affiliation:
Research Centre for Modeling & Simulation, National University of Sciences & Technology, Islamabad, Pakistan
A. Maqsood
Affiliation:
Research Centre for Modeling & Simulation, National University of Sciences & Technology, Islamabad, Pakistan
A.J. Hashmi
Affiliation:
Research Centre for Modeling & Simulation, National University of Sciences & Technology, Islamabad, Pakistan
M.T. Saeed
Affiliation:
Research Centre for Modeling & Simulation, National University of Sciences & Technology, Islamabad, Pakistan
R. Riaz
Affiliation:
Research Centre for Modeling & Simulation, National University of Sciences & Technology, Islamabad, Pakistan
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Abstract

This paper discusses the utilisation of deep reinforcement learning algorithms to obtain optimal paths for an aircraft to avoid or minimise radar detection and tracking. A modular approach is adopted to formulate the problem, including the aircraft kinematics model, aircraft radar cross-section model and radar tracking model. A virtual environment is designed for single and multiple radar cases to obtain optimal paths. The optimal trajectories are generated through deep reinforcement learning in this study. Specifically, three algorithms, namely deep deterministic policy gradient, trust region policy optimisation and proximal policy optimisation, are used to find optimal paths for five test cases. The comparison is carried out based on six performance indicators. The investigation proves the importance of these reinforcement learning algorithms in optimal path planning. The results indicate that the proximal policy optimisation approach performed better for optimal paths in general.

Keywords

Path planningReinforcement learningRadar avoidance

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1297 , March 2022 , pp. 547 - 564
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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