Effect of Beam Pattern and Amplifier Gain of Repeater-type Active Decoy on Jamming to Active RF Seeker System based on Proportional Navigation Law

Rim, Jae-Won; Koh, Il‐Suek

doi:10.23919/irs.2018.8448186

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…A method to simulate echo signal at the tracking radar is proposed in [11], and the effects of circular and linear polarization of signals are analysed for repeater-type active decoys against ground tracking radar. Jamming performance of an active repeater decoy is evaluated in [12] based on RF specifications of the decoy, such as antenna patterns, and amplifier gains. In [1«] , a ducted-fan flight array system is allocated for the role of decoy to guarantee the protection of the target ship against anti-ship missiles (ASMs).…”

Section: Introductionmentioning

confidence: 99%

Development of Reinforcement Learning Based Mission Planning Method for Active Off-board Decoys on Naval Platforms

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Tsourdos

et al. 2022

AIAA SCITECH 2022 Forum

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In this paper, reinforcement learning based decoy deployment strategy is proposed to protect naval platforms against radar seeker equipped anti-ship missiles. Decoy system consists of a rotary-wing unmanned aerial vehicle (UAV) and an integrated onboard jammer. This decoy concept enables agility which is quite critical for jamming operations against a high-speed anti-ship missile. There are two main purposes of the developed jamming strategy; a) flying in the field of view of the anti-ship missile to conceal the naval platform, and b) flying away from the target ship to increase the miss distance between the anti-ship missile and naval platform. Here, it is aimed to meet these requirements simultaneously. Kinematics models are used to represent missile, decoy UAV and target motion. Jammer and seeker signal strengths are modeled and radar-cross section of a frigate is utilized to increase the realism of the simulation environment. Deep Deterministic Policy Gradient (DDPG) algorithm is applied to train an actor-critic agent which maps the observation parameters to decoy's lateral acceleration. A heuristic way is chosen to create appropriate reward function to solve the decoy guidance problem. Finally, simulations studies are performed to evaluate the system performance.

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Section: Introductionmentioning

confidence: 99%

Development of Reinforcement Learning Based Mission Planning Method for Active Off-board Decoys on Naval Platforms

Bildik

Yüksek

Tsourdos

et al. 2022

AIAA SCITECH 2022 Forum

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Development of Active Decoy Guidance Policy by Utilising Multi-Agent Reinforcement Learning

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Tsourdos

et al. 2023

AIAA SCITECH 2023 Forum

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Decoys Deployment for Missile Interception: A Multi-Agent Reinforcement Learning Approach

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Tsourdos,

Perrusquía

et al. 2024

Aerospace

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Recent advances in radar seeker technologies have considerably improved missile precision and efficacy during target interception. This is especially concerning in the arenas of protection and safety, where appropriate countermeasures against enemy missiles are required to ensure the protection of naval facilities. In this study, we present a reinforcement-learning-based strategy for deploying decoys to enhance the survival probability of a target ship against a missile threat. Our approach involves the coordinated operation of three decoys, trained using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithms. The decoys operate in a leader–follower dynamic with a circular formation to ensure effective coordination. We evaluate the strategy across various parameters, including decoy deployment regions, missile launch directions, maximum decoy speeds, and missile speeds. The results indicate that, decoys trained with the MATD3 algorithm demonstrate superior performance compared to those trained with the MADDPG algorithm. Insights suggest that our decoy deployment strategy, particularly when utilizing MATD3-trained decoys, significantly enhances defensive measures against missile threats.

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Effect of Beam Pattern and Amplifier Gain of Repeater-type Active Decoy on Jamming to Active RF Seeker System based on Proportional Navigation Law

Cited by 4 publications

References 6 publications

Development of Reinforcement Learning Based Mission Planning Method for Active Off-board Decoys on Naval Platforms

Development of Reinforcement Learning Based Mission Planning Method for Active Off-board Decoys on Naval Platforms

Development of Active Decoy Guidance Policy by Utilising Multi-Agent Reinforcement Learning

Decoys Deployment for Missile Interception: A Multi-Agent Reinforcement Learning Approach

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