Intelligent time-series signal classification method based on gated recurrent unit deep network

Abstract

The invention discloses an intelligent time-series signal classification method based on a deep network of gated cyclic units. The implementation steps are: (1) constructing a coding-modulation joint time-series signal; (2) generating a training sample set and a test sample set; (3) building Gated recurrent unit deep network model; (4) setting the parameters of the gated recurrent unit deep network; (5) training the gated recurrent unit deep network model; (6) obtaining classification accuracy. The present invention does not require manual feature extraction and a large amount of prior knowledge, can automatically feature extraction and accurate signal classification for one-dimensional signals, has the advantages of low complexity, accurate and stable classification results, and can be used in military and civilian communication fields .

Description

technical field [0001] The invention belongs to the technical field of communication, and further relates to an intelligent timing signal classification method based on a deep network of gated cyclic units in the technical field of signal processing. The invention can automatically extract the characteristics of the radio time series signal and perform coding and modulation classification through the gated cycle unit, so that the classification of the radio signal has a higher degree of automation and intelligence. Background technique [0002] Radio signal classification technology plays an important role in communication systems. In the field of military communication countermeasures, it is generally necessary to interfere and intercept enemy communications. The identification and classification of radio signal modulation methods is the first problem to be faced in interference and interception. In the field of civil communications, radio spectrum monitoring and managemen...

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Problems solved by technology

The shortcomings of this method are: although this method proposes a communication signal identification method, it needs to perform signal truncation and high-order cumulant processing on the signal to be tested, and a large amount of prior knowledge is required when performing signal feature extraction. Knowledge, human factors have a great influence on feature extraction

Although this method proposes a radio signal recognition method and its implementation system based on a deep learning model, the method still has the disadvantages that the model is complex, and the signal must be transformed in the time-frequency domain before subsequent processing The problem

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