LPI Radar Signal Recognition with Convolution Feature and Discrimination Dictionary Learning

doi:10.3969/j.issn.1000-1093.2019.09.013

Abstract

Abstract: The selection of artificial features, low signal-to-noise ratio and small number of samples lead to low recognition rate for low probability of intercepting radar signal. A recognition algorithm with convolution feature and discrimination dictionary learning is proposed. The proposed algorithm is based on the time-frequency image representing a signal modulation type, and a two-dimensional signal is obtained by time-frequency transformation, which is input into LeNet-5. The network is retrained through MNIST data set. The network parameters of 2-6 layers are transferred to a new LeNet-5, and the data from the 6th convolution layer is extracted as convolutional feature. Finally, recognition is ended up by discrimination dictionary learning. Simulated results show that the network goes faster in convergence and optimization through pre-training, and can effectively extract the convolution feature of each kind of signal. Higher recognition rate is obtained through discrimination dictionary learning in the condition of low SNR and small samples compared with other algorithms. Key

Key words: radarsignal, lowprobabilityofintercept, convolutionalneuralnetwork, convolutionalfeature, dictionarylearning, signalrecognition

CLC Number:

TN971⁺.1

GUO Pengcheng, WU Liyang. LPI Radar Signal Recognition with Convolution Feature and Discrimination Dictionary Learning[J]. Acta Armamentarii, 2019, 40(9): 1881-1889.

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第40卷
第9期2019 年9月兵工学报ACTA
ARMAMENTARIIVol.40No.9Sep.2019

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