Intelligent time sequence signal classification method based on gate control circulation unit deep network

Abstract

The invention discloses an intelligent time sequence signal classification method based on gate control circulation unit deep network. The intelligent time sequence signal classification method comprises steps of (1) establishing of code modulation combined time sequence signals; (2)generating of a training sample set and a testing sample set; (3) establishing of a gate control circulation unit deep network model; (4)the setting of the parameters of the gate control circulation unit deep network; (5) the training of the gate control circulation unit deep network model; (6) acquisition of classification accuracy. Artificial characteristic extraction and massive priori knowledge are not required, and automatic characteristic extraction of one-dimensional signals and accurate signal classification can be realized, and therefore complexity is low, classification results are accurate and stable, and the intelligent time sequence signal classification method can be used for military and civil 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...

AI Technical Summary

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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