An efficient deep convolutional neural network with features fusion for radar signal recognition

Si, Weijian; Wan, Chenxia; Deng, Zhongliang

doi:10.1007/s11042-022-13407-9

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…It even reached 94.31% at an SNR of -10 dB. Especially in low SNR environments, the recognition accuracy of this method was significantly better than others [ 7 ]. For bringing improvement to the manual detection of exudates in fundus images, E. Dhiravidachelvi et al proposed a new image detection method combining CNN-RNN with an artificial bee colony optimization algorithm.…”

Section: Related Workmentioning

confidence: 92%

Signal automatic modulation based on AMC neural network fusion

Yin,

Diao

2024

PLoS ONE

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With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further improve the communication quality and system processing efficiency, this study combines two different neural network algorithms to optimize the traditional signal automatic modulation classification method. In this paper, the basic technology involved in the communication process, including automatic signal modulation technology and signal classification technology, is discussed. Then, combining parallel convolution and simple cyclic unit network, three different connection paths of automatic signal modulation classification model are constructed. The performance test results show that the classification model can achieve a stable training and verification state when the two networks are connected. After 20 and 29 iterations, the loss values are 0.13 and 0.18, respectively. In addition, when the signal-to-noise ratio (SNR) is 25dB, the classification accuracy of parallel convolutional neural network and simple cyclic unit network model is as high as 0.99. Finally, the classification models of parallel convolutional neural networks and simple cyclic unit networks have stable correct classification probabilities when Doppler shift conditions are introduced as interference in practical application environment. In summary, the neural network fusion classification model designed can significantly improve the shortcomings of traditional automatic modulation classification methods, and further improve the classification accuracy of modulated signals.

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Section: Related Workmentioning

confidence: 92%

Signal automatic modulation based on AMC neural network fusion

Yin,

Diao

2024

PLoS ONE

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“…In [31], a multi-class learning framework based on CNN under the same SNR was proposed, and the results demonstrated superior performance over others. In [32], an efficient deep CNN with feature fusion at low SNR was presented, and recognition performance was up to 84.38% at −12 dB. Although the CNN-based network has demonstrated better robustness in the single-component radar signal, it cannot be applied completely to dual-component radar signals.…”

Section: Radar Signal Modulation-recognition Methods Based On Cnnmentioning

confidence: 99%

A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST

Wan,

Zhang

2024

Applied Sciences

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Dual-component radar-signal modulation recognition is a challenging yet significant technique for electronic reconnaissance systems. To improve the lower recognition performance and the higher computational costs of the conventional methods, this paper presents a randomly overlapping dual-component radar-signal modulation recognition method based on a convolutional neural network–swin transformer (CNN-ST) under different signal-to-noise ratios (SNRs). To enhance the feature representation ability and decrease the loss of the detailed features of dual-component radar signals under different SNRs, the swin transformer is adopted and integrated into the designed CNN model. An inverted residual structure and lightweight depthwise convolutions are used to maintain the powerful representational ability. The results show that the dual-component radar-signal recognition accuracy of the proposed CNN-ST is up to 82.58% at −8 dB, which shows the better recognition performance of the CNN-ST over others. The dual-component radar-signal recognition accuracies under different SNRs are all more than 88%, which verified the fact that the CNN-ST achieves better recognition accuracy under different SNRs. This work offers essential guidance in enhancing dual-component radar signal recognition under different SNRs and in promoting actual applications.

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“…In [31], a multi-class learning framework based on CNN under the same SNR was proposed, and the results demonstrated superior performance over others. In [32], an e cient deep CNN with features fusion at low SNR was presented, and recognition performance was up to 84.38% at -12 dB.…”

Section: Radar Signal Modulation Recognition Methods Based On Cnnmentioning

confidence: 99%

A novel dual-component radar signals modulation recognition method based CNN-ST

Wan

2023

Preprint

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This paper presents a randomly overlapping dual-component radar signals recognition method based on a convolutional neural network-swin transformer (CNN-ST) under different signal-to-noise ratio (SNR), for improving the lower recognition performance and the higher computational costs of the conventional methods. To enhance the feature representation ability and decrease the loss of the detailed features of dual-component radar signals under different SNR, the swin transformer is adopted and integrated into the designed CNN model. An inverted residual structure and lightweight depthwise convolutions are used to maintain the powerful representational ability. The results show that the dual-component radar signals recognition accuracy of the proposed CNN-ST is up to 82.58% at -8 dB, which shows the better recognition performance of the CNN-ST over others. The dual-component radar signals recognition accuracies under different SNR are all more than 88%, which verified that the CNN-ST achieves better recognition accuracy under different SNR. The recognition performance of 2FSK-EQFM, 2FSK-LFM, 2FSK-NS, EQFM-LFM, EQFM-NS, and LFM-NS are up to 94.44%, 93.33%, 88.89%, 95.56%, 87.78%, and 94.44% at SNR of -8 dB, respectively. This work offers essential guidance in enhancing dual-component radar signals recognition under different SNR and promoting actual applications.

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An efficient deep convolutional neural network with features fusion for radar signal recognition

Cited by 7 publications

References 28 publications

Signal automatic modulation based on AMC neural network fusion

Signal automatic modulation based on AMC neural network fusion

A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST

A novel dual-component radar signals modulation recognition method based CNN-ST

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