Towards an accurate radar waveform recognition algorithm based on dense CNN
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Towards an accurate radar waveform recognition algorithm based on dense CNN Weijian Si 1 & Chenxia Wan 1
& Chunjie Zhang
1
Received: 1 April 2020 / Revised: 26 June 2020 / Accepted: 28 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Radar waveform recognition . Time frequency distribution . Convolutional neural network . Transfer learning
1 Introduction Automatic radar waveform recognition technology can be capable of identifying the low probability of intercept (LPI) radar waveform of received signal, which plays an essential role
* Chunjie Zhang [email protected]
1
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Multimedia Tools and Applications
in electronic warfare systems, such as electronic support, electronic intelligence, electronic attack, and so on [1, 13]. With the remarkable development of radar technology and the working environment of radar at the lower SNR in the recent decades, the modulation types of LPI radar signal have become more and more complicated and diversified [24]. Therefore, it is crucial to explore a more accurate approach to recognize the radar waveform at the lower SNR environment. Some LPI waveform automatic recognition techniques have been proposed in recent years [2], which utilized feature extraction and classification techniques to extract features from the LPI radar signal and classify the types of signal, respectively. For the feature extraction, time-frequency conversion technology can transform the signal waveform into time-frequency images (TFI), such as wigner ville distribution (WVD) [11, 28] and choiwillian d
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