Modulation signal identification method based on course learning

Abstract

The invention discloses a modulation signal identification method based on course learning, and mainly solves the problem of low identification rate caused by signal noise in the prior art. Accordingto the scheme, the method comprises the steps of acquiring a trained modulation signal sampling sequence and corresponding mark data, and preprocessing the sampling sequence; constructing a deep residual network; taking the preprocessed sampling sequence as the input of a deep residual network, taking the mark data of the sampling sequence as the modulation type corresponding to the maximum component in the output vector of the deep residual network, and training the constructed deep residual network by utilizing a training strategy of course learning to obtain a trained network; and taking the modulation signal grey-scale map to be identified as the input of the trained network, wherein the modulation type corresponding to the maximum component in the network output vector is the identified modulation type. According to the method, the training speed is increased, the influence of too strong signal noise on the recognition rate is reduced, the modulation recognition performance in a strong noise environment is improved, and the method can be used for electronic countermeasure and radio management.

Description

technical field [0001] The invention belongs to the technical field of communication, in particular to a modulated signal identification method, which can be used in electronic countermeasures and radio management. Background technique [0002] The purpose of the automatic modulation recognition task is to detect the modulation type of the received signal and recover the signal through demodulation. Currently common digital modulation signals include amplitude shift keying ASK, frequency shift keying FSK, phase shift keying PSK and quadrature amplitude modulation QAM. Modulation recognition has been widely used in electronic warfare, surveillance, and threat analysis. Among them, the likelihood-based detection method and the feature extraction method are two commonly used automatic modulation recognition methods. The detection methods based on the likelihood ratio mainly include the average likelihood ratio test method and the generalized likelihood ratio test. Although t...

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Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the prior art above, to provide a modulation signal recognition method based on curriculum learning, to guide the training of deep residual network with the training strategy of curriculum learning, to avoid the network from overfitting the data noise, Make the network learn a more robust model faster, improve the recognition performance in a strong noise environment, and avoid the problem of low recognition rate caused by signal noise in the existing network

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