Research on Radar Active Deception Jamming Identification Method Based on RESNET and Bispectrum Features

Wang, Kunteng; Dong, Zeyu; Wan, Tao; Jiang, Kaili; Xiong, Wanan; Fang, Xueli

doi:10.1109/iccea53728.2021.00102

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The transient signal of the transmitter is not covered by the modulated signal with strong energy, and the nonlinear characteristics of the transient phase are very strong, and there are many characteristics reflecting individual differences. It is usually used to analyze burst signals [6]. In [7], a transient signal RFF recognition scheme based on logarithmic power cosine spectrum is proposed.…”

Section: A Features Extractionmentioning

confidence: 99%

Specific Emitter Identification of Frequency Hopping Signals Based on Feature Extraction and Deep Residual Network

Xie

Yang

et al. 2022

IEEE Access

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In the modern war with complex and changeable electromagnetic environment, Specific Emitter Identification (SEI) is an important and difficult problem, which is of great significance in obtaining intelligence information, identifying ourselves or foe, and specifying combat plans. In order to solve the problems of low accuracy, poor robustness and difficulty in extracting effective individual features of frequency hopping (FH) radio set, this paper studies the generation of PSK signal constellation to proposes a radio frequency fingerprint(RFF) based on constellation. We also improved the existing RFF extraction method, combined with the deep learning recognition method based on the Deep Residual Network (ResNet), to achieve effective feature fusion. By comparing different input methods, we found that the recognition accuracy is improved, and reaches 96.16% in the outfield experiments of 15 FH radio sets. In addition, we also designed a ResNet structure to compare the recognition accuracy under different signal-to-noise ratio(SNR), different network structure, different number of individuals, different modulation methods and different recognition algorithms, which proved the superiority of our method.

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Section: A Features Extractionmentioning

confidence: 99%

Specific Emitter Identification of Frequency Hopping Signals Based on Feature Extraction and Deep Residual Network

Xie

Yang

et al. 2022

IEEE Access

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“…A weighted integrated CNN radar jamming recognition algorithm based on transfer learning was proposed in 2021 for the low recognition accuracy of deep learning jamming recognition methods in the case of small samples [14]. A jamming recognition method based on Residual Neural Network (ResNet) was proposed in 2021, using the bispectrum diagonal slice extracted from the jamming signals as the input of ResNet to realize the recognition of active deception jamming [15].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks

et al. 2022

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Jamming recognition is an essential step in radar detection and anti-jamming in the complex electromagnetic environment. When radars detect an unknown type of jamming that does not occur in the training set, the existing radar jamming recognition algorithms fail to correctly recognize it. However, these algorithms can only recognize this type of jamming as one that already exists in our jamming library. To address this issue, we present two models for radar jamming open set recognition (OSR) that can accurately classify known jamming and distinguish unknown jamming in the case of small samples. The OSR model based on the confidence score can distinguish known jamming from unknown jamming by assessing the reliability of the sample output probability distribution and setting thresholds. Meanwhile, the OSR model based on OpenMax can output the probability of jamming belonging to not only all known classes but also unknown classes. Experimental results show that the two OSR models exhibit high recognition accuracy for known and unknown jamming and play a vital role in sensing complex jamming environments.

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Research on Active Jamming Recognition Method Based on Fractional Fourier Transform

Lin

Gao

2022

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )

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Research on Radar Active Deception Jamming Identification Method Based on RESNET and Bispectrum Features

Cited by 4 publications

References 8 publications

Specific Emitter Identification of Frequency Hopping Signals Based on Feature Extraction and Deep Residual Network

Specific Emitter Identification of Frequency Hopping Signals Based on Feature Extraction and Deep Residual Network

Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks

Research on Active Jamming Recognition Method Based on Fractional Fourier Transform

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