Underwater acoustic communication signal modulation mode identification method based on recurrent neural network

Abstract

The invention provides an underwater acoustic communication signal modulation mode identification method based on a recurrent neural network. The method comprises steps of acquiring underwater acoustic communication signal simulation data or actual measurement data, and dividing the data into a training set and a test set; sampling each signal data, and carrying out standardization and normalization processing; extracting instantaneous frequency features and spectral entropy features from the data samples of the training set and the test set respectively; labeling data samples of the trainingset and the test set with labels; establishing a Bi-LSTM recurrent neural network model and setting parameters; inputting the training set into a network model, and training to obtain optimal trainingnetwork parameters; and switching the input of the deep learning recurrent neural network into a test set, and verifying automatic identification of the network. According to the method, the characteristic that the underwater acoustic communication signals have time sequence is adopted, the Bi-LSTM recurrent neural network capable of processing the time sequence input sequence is adopted, the network suitable for the underwater communication signals is obtained through training, and the network has a high recognition rate for the non-cooperative underwater acoustic communication signals.

Description

technical field [0001] What the present invention relates to is a kind of underwater acoustic signal processing method, exactly a kind of non-cooperative underwater acoustic communication signal modulation pattern recognition method based on recurrent neural network (RNN). Background technique [0002] Identifying the modulation mode of non-cooperative underwater acoustic communication signals faces difficulties such as low signal-to-noise ratio, no prior information, complex classifier design, and low accuracy of manual identification. The pattern recognition method based on the maximum likelihood ratio hypothesis test requires known prior information and is not suitable for non-cooperative underwater acoustic communication signals; the pattern recognition method based on feature extraction has the problem that the feature dimension is low and the classification effect is not obvious; Some pattern recognition methods based on deep learning are mostly applied in the radio fi...

AI Technical Summary

Problems solved by technology

[0002] Identifying the modulation mode of non-cooperative underwater acoustic communication signals faces difficulties such as low signal-to-noise ratio, no prior information, complex classifier design, and low accuracy of manual identification.

The pattern recognition method based on the maximum likelihood ratio hypothesis test requires known prior information and is not suitable for non-cooperative underwater acoustic communication signals; the pattern recognition method based on feature extraction has the problem that the feature dimension is low and the classification effect is not obvious; Some pattern recognition methods based on deep learning are mostly applied in the radio field, and do not make full use of the timing characteristics of underwater acoustic communication signals or the network architecture parameters are not suitable for non-cooperative underwater acoustic communication signals and the recognition effect is not good

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