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

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Abstract

This paper proposes an efficient deep convolutional neural network with features fusion for recognizing radar signal, which mainly includes data pre-processing, features extraction, multi-features fusion, and classification. Radar signals are first transformed into time-frequency images by using choi-williams distribution and smooth pseudo-wigner-ville distribution, and the image pre-processing methods are used to resize and normalize the time-frequency images. Then, two constructed deep convolutional neural network models are aimed to extract more effective features. Furthermore, a multi-features fusion model is proposed to integrate features extracted from two deep convolutional neural network models, which makes full use of the relationship among different features and further improves the recognition performance. Experimental results shown that the average recognition accuracy of the proposed method is up to 84.38% when the signal to noise ratio is at −12 dB, and even reach to 94.31% at −10 dB, which achieved the superior recognition performance than others, especially at the lower signal to noise ratio. Moreover, the recognition performance of various radar signals can be largely improved, especially for 2FSK, 4FSK and SFM. This work provides a sound experimental foundation for further improving radar signal recognition in modern electronic warfare systems.

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Acknowledgments

This work was financially supported in part by the National Natural Science Foundation of China (Grant No. 61671168 and 61801143), in part by the National Natural Science Foundation of Heilongjiang Province (Grant No. JJ2019LH1760 and LH2020F019), in part by the Aeronautical Science Foundation of China (Grant No. 2019010P6001 and 2019010P6002), and in part by the Fundamental Research Funds for the Central Universities (Grant No. HEUCFJ180801).

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Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Weijian Si, Chenxia Wan & Zhian Deng

  2. Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, 150001, China

    Weijian Si, Chenxia Wan & Zhian Deng

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  1. Weijian Si
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  2. Chenxia Wan
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Correspondence to Zhian Deng.

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Si, W., Wan, C. & Deng, Z. An efficient deep convolutional neural network with features fusion for radar signal recognition. Multimed Tools Appl 82, 2871–2885 (2023). https://doi.org/10.1007/s11042-022-13407-9

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  • DOI: https://doi.org/10.1007/s11042-022-13407-9

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