Signal modulation recognition algorithm based on data enhancement and convolutional neural network

Abstract

In order to improve the accuracy of modulation recognition, the invention provides a signal modulation recognition algorithm based on data enhancement and a convolutional neural network. The method comprises the following steps: firstly, processing a time domain signal in a data set RML2016.10a into a constellation diagram, and dividing pictures into a training set and a test set according to a proportion of 8: 2; secondly, processing the training sets by using four data enhancement methods of rotation, random erasing, overturning and CutMix to obtain four enhanced training sets; respectively inputting the four types of enhanced training sets into a GoogleNet network for training, and continuously optimizing network parameters according to back propagation to obtain a trained network; and finally, sending the test set into the trained GoogleNet network for testing, drawing a curve of which the correct rate changes along with the signal-to-noise ratio, and comparing the improvement effects of different data enhancement methods on the correct recognition rate to obtain an optimal data enhancement method. Compared with other modulation recognition algorithms without data enhancement, the method has the advantages that the risk of model overfitting is reduced, and the generalization ability of the model is improved.

Description

technical field [0001] The convolutional neural network related algorithm involved in the present invention belongs to the field of computer vision and artificial intelligence, the data enhancement belongs to the field of image processing, and the signal modulation recognition belongs to the field of communication technology. Background technique [0002] Automatic Modulation Classification (AMC) refers to the process of knowing the modulation set of the signal, using information technology and computer technology to analyze the modulated target signal to determine the signal modulation type, which is used in both military and civilian fields. Has quite a wide range of applications. In the military field, automatic modulation classification can be applied to applications such as communication countermeasures and electronic reconnaissance. During communication countermeasures, it is necessary to detect, interfere and demodulate enemy signals to steal important enemy intellige...

AI Technical Summary

Problems solved by technology

[0005] Although deep learning based methods can greatly improve the performance of modulation classifiers, it requires a large amount of training data

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