Radar waveform recognition based on time and phase features convolutional network
Low probability of intercept(LPI)radars are widely employed due to their excellent anti-intercep-tion capabilities,and the identification of LPI radar waveforms is essential for electronic reconnaissance systems.However,the existing deep learning-based method for LPI radar waveform recognition faces challenges,such as large network parameters and high computational complexity.These issues significantly limit its applicability in re-source-constrained scenarios.Addressing the need for swift and accurate LPI radar waveform recognition,a novel radar waveform recognition model based on a time-phase feature convolutional network is introduced.Unlike tra-ditional time-frequency transformation methods,this approach utilizes a convolutional neural network to directly extract phase and short-time features from the raw signal.This results in a lightweight and low-complexity model.Validation through 13 LPI radar waveform recognition experiments demonstrates that the proposed method achieves over 90%accuracy even at a signal-to-noise ratio of-4 dB.Compared to recognition methods based on the Wigner-Ville transform and image deep networks,The presented method requires only 12%of the parame-ters and 0.14%of the computational resources to achieve equivalent accuracy which strikes a balance between ac-curacy and processing speed,exhibiting promising potential for practical engineering applications.
low probability of intercept radarwaveform recognitiondeep learningconvolutional neural networkphase and short-time feature
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