Interference Resource Scheduling Algorithm Based on Potential Game for UAV Cooperative Combat

Deng, Min; Wu, Zhigao; Chen, Yongqi

doi:10.1109/icct52962.2021.9657867

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Such as refs. [6][7][8]. Both S. H. Won [6] and M. Deng [7] propose an idea to allocate interference resources in different specific backgrounds by setting relevant constraint functions.…”

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confidence: 99%

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Adaptive intra‐pulse interference waveform generation technique based on Convolutional Neural Network—Deep Neural Network with global priority information of radar signal

Jiang

Shang

Guan

et al. 2022

IET Radar Sonar & Navi

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To adapt to the complex and changeable electromagnetic environment of radar detection, improve the jamming effect on frequency‐agile radar and low probability of intercept radar, and solve the problem of poor jamming effect caused by intra‐pulse jamming lagging behind target radar signal, a Convolutional Neural Network—Deep Neural Network jamming waveform generation method, based on prior global information of radar signal, is presented in this study. This proposed method uses two networks to generate the overall interference waveforms. One Convolutional Neural Network—Deep Neural Network derives prior global radar information from local prior radar information, which is used by the other Convolutional Neural Network—Deep Neural Network to generate interference. In the whole scheme, an algorithm to design the intra‐pulse interference waveform based on the prior global information of the radar signal is proposed. The algorithm can generate the interference waveform samples from the number and area of multiple false targets and the relative peak between multiple false targets and the real target area by the relevant parameters. Then, an evaluation system (Matched filter, Constant False Alarm Rate Detector, and Distance resolution etc.) is established to evaluate the global effect of the designed interference waveforms. Finally, the time‐domain Root Mean Squared Errors is used as the loss function to optimise the training of the network and ultimately achieve the requirement of generating intra‐pulse adaptive interference waveforms based on radar signal fragments. The principle of generating interference waveforms is based on various aspects of the evaluation of the interference effect, which enhances the time domain correlation between multiple false target areas, thereby boosting the interference effect on the radar. The experimental results show that the proposed method based on the Convolutional Neural Network—Deep Neural Network can effectively solve the problem of intra‐pulse interference lag and small effective interference area after the pulse pressure of the interference waveform matching filter. This study offers certain reference significance for the adaptive interference waveform design based on radar prior global information.

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“…Such as refs. [6][7][8]. Both S. H. Won [6] and M. Deng [7] propose an idea to allocate interference resources in different specific backgrounds by setting relevant constraint functions.…”

mentioning

confidence: 99%

“…[6][7][8]. Both S. H. Won [6] and M. Deng [7] propose an idea to allocate interference resources in different specific backgrounds by setting relevant constraint functions. C. G. Shi [8] said that the study is actually an optimisation problem of minimising an objective, using the mutual information criterion to optimise the interference power.…”

mentioning

confidence: 99%