Cooperative cognitive electronic warfare UAV game modeling for frequency hopping radar

Rahmes, Mark; Chester, Dave; Clouse, Rich; Hunt, Jodie; Ottoson, Tom

doi:10.1117/12.2291554

Cited by 1 publication

(3 citation statements)

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“…A new type of network using a dual-controller generation policy was proposed. Rahmes Mark et al designed a multi-UAV countermeasure system and used Q-Learning algorithm to generate radar transmitting frequency [18]. Cevik Polat et al reduced the resource allocation problem in electronic countermeasures to an optimal allocation problem, and proposed a resource allocation algorithm based on particle swarm optimization theory [19].…”

Section: Related Workmentioning

confidence: 99%

“…That is to say, 100m of y-axis and 1m of x-axis have the same length in Figure 7. [2,18], [4,18], [6,18], [8,18], [10,18], [12,18] [x (1 m),y (100 m)]…”

Section: Map Griddingmentioning

confidence: 99%

“…The width of both mainlobe is 50 • . The relay position is always at [6,18]. The scanning mode of beam pointing is from right to left, and then from left to right.…”

Section: Collaborative Target Rangingmentioning

confidence: 99%

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Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

Wang

et al. 2019

Applied Sciences

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The networked unmanned aerial vehicle (UAV) radar system may exploit inter-UAV cooperation for enhancing information acquisition capabilities, meanwhile its inter-UAV communications may be interfered with by external jammers. This paper is devoted to quantifying and optimizing the anti-jamming performance of networked UAV radar systems in adversarial electromagnetic environments. Firstly, instead of using the conventional metric of signal-to-interference ratio (SIR), this paper explores use of the theory of radar information representation as the basis of evaluating the information acquisition capabilities of the networked UAV radar systems. Secondly, this paper proposes a modified Q-Learning method based on double greedy algorithm to optimize the anti-jamming performance of the networked UAV radar systems, through joint programming in the frequency-motion-antenna domain. Simulation results prove the effectiveness of the algorithm in two different networking scenarios.

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Section: Related Workmentioning

confidence: 99%

“…That is to say, 100m of y-axis and 1m of x-axis have the same length in Figure 7. [2,18], [4,18], [6,18], [8,18], [10,18], [12,18] [x (1 m),y (100 m)]…”

Section: Map Griddingmentioning

confidence: 99%

See 1 more Smart Citation

Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

Wang

et al. 2019

Applied Sciences

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Cooperative cognitive electronic warfare UAV game modeling for frequency hopping radar

Cited by 1 publication

References 1 publication

Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

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