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Towards an accurate radar waveform recognition algorithm based on dense CNN

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Abstract

Existing algorithms for radar waveform classification currently exhibit the lower recognition accuracy, especially at the lower signal to noise ratio (SNR) environment. To remedy these flaws, this paper proposes an accurate automatic modulation classification algorithm based on dense convolutional neural networks (AAMC-DCNN). The algorithm owns the competitive advantages of strengthening the feature reuse and extracting the detailed feature, for improving the recognition performance of radar waveform at the lower SNR. First, the dense convolutional neural networks (CNN) are designed, which connects each layer to every other layer in a feed-forward pattern. In the latter, 8 types of signals are converted into time-frequency images by choi-williams distribution (CWD), and the large training and testing datasets are fabricated. Then, the transfer learning and Adam optimization are introduced. Finally, the experimental analyses are carried out to evaluate the recognition performance. It is worth mentioning that the classification accuracy can be up to 93.4% when the SNR is −8 dB, and even reach to 100% at 0 dB, which demonstrates the superior performance over others. The present work provides a sound experimental basis for further studying automatic modulation classification for the sake of future field application in 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. QC2016085), and in part by the Fundamental Research Funds for the Central Universities (Grant No. HEUCFJ180801).

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

    Weijian Si, Chenxia Wan & Chunjie Zhang

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Correspondence to Chunjie Zhang.

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Si, W., Wan, C. & Zhang, C. Towards an accurate radar waveform recognition algorithm based on dense CNN. Multimed Tools Appl 80, 1779–1792 (2021). https://doi.org/10.1007/s11042-020-09490-5

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