Abstract
Low Probability of Intercept (LPI) radar waveform recognition is not only an important branch of the electronic reconnaissance field, but also an important means to obtain non-cooperative radar information. To solve the problems of LPI radar waveform recognition rate, difficult feature extraction and large number of samples needed, an automatic classification and recognition system based on Choi-Williams distribution (CWD) and depth convolution neural network migration learning is proposed in this paper. First, the system performs CWD time-frequency transform on the LPI radar waveform to obtain a 2-D time-frequency image. Then the system preprocesses the original time-frequency image. In addition, then the system sends the pre-processed image to the pre-training model (Inception-v3 or ResNet-152) of the deep convolution network for feature extraction. Finally, the extracted features are sent to a Support Vector Machine (SVM) classifier to realize offline training and online recognition of radar waveforms. The simulation results show that the overall recognition rate of the eight LPI radar signals (LFM, BPSK, Costas, Frank, and T1–T4) of the ResNet-152-SVM system reaches 97.8%, and the overall recognition rate of the Inception-v3-SVM system reaches 96.2% when the SNR is −2 dB.
Highlights
Low Probability of Intercept (LPI) radar has the characteristics of high resolution, low probability of intercept, time width bandwidth product, and strong anti-jamming ability, which makes it difficult to detect the traditional non-cooperative intercept receiver
How to identify LPI radar signal waveform effectively becomes the focus of non-cooperative radar signal processing research [1,2,3]
In [9], the method of extracting radar signal waveform features based on Choi-Williams distribution (CWD) time-frequency transform and image processing is proposed combined with ENN neural network classification and recognition
Summary
Low Probability of Intercept (LPI) radar has the characteristics of high resolution, low probability of intercept, time width bandwidth product, and strong anti-jamming ability, which makes it difficult to detect the traditional non-cooperative intercept receiver. In [9], the method of extracting radar signal waveform features based on CWD time-frequency transform and image processing is proposed combined with ENN neural network classification and recognition. The recognition rate of radar waveforms (LFM, BPSK, Costas, Frank code, P1–P4 code) is 94.7% under the condition of SNR −2 dB These methods do not make full use of other features of the image. The LPI radar waveform recognition based on deep convolution network transfer learning is proposed, which would solve the problems of difficult extraction of traditional CNN LPI radar waveform features, large training samples, complicated time-frequency image pre-processing and low recognition rate of various waveforms under low SNR. The method uses ImageNet trained pre-training models (interception-v3 and ResNet-152) to automatically extract waveform features This method improves the recognition accuracy, and reduces the number of training samples.
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