Novel electromagnetic signal identification method and device for graph convolution network and transfer learning

Abstract

The invention provides a novel electromagnetic signal identification method and device based on a graph convolution network and transfer learning, and the method comprises the steps: constructing a graph structure based on the implicit knowledge of an electromagnetic signal; constructing a graph convolutional neural network, obtaining a category weight vector to which the novel electromagnetic signal belongs, and constructing an updated electromagnetic signal classification weight matrix; extracting a deep feature vector of the novel electromagnetic signal to be identified; and according to the updated electromagnetic signal classification weight matrix and the deep feature vector, completing transfer learning of the novel electromagnetic signal, and generating a perception identificationresult of the novel electromagnetic signal. The method can recognize the novel electromagnetic signal based on the graph convolution network and transfer learning, and effectively guarantees the recognition precision of the novel electromagnetic signal for a target, the robustness for the conversion of a scene and sensing equipment, the recognition response speed, and the adaptive capability whena new target appears.

Description

technical field [0001] The invention relates to the technical field of electromagnetic signal intelligent perception, in particular to a novel electromagnetic signal recognition method and device for graph convolutional network and transfer learning. Background technique [0002] With the rapid development of electronic components and the emergence of various new signal processing technologies, various new and complex radiation source signals in the modern electromagnetic environment are increasing day by day. Wide frequency range, diverse modulation types, flexible signal processing capabilities, and increasingly dense radiation source signal flow make the current electromagnetic environment more and more complex, and electromagnetic signals are becoming more and more flexible and diverse. is an unknown signal. [0003] In the existing technology, the traditional identification methods are based on feature matching, statistical decision theory or support vector machine cla...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, the traditional identification methods are based on feature matching, statistical decision theory or support vector machine classification, etc. Most of them rely on manual design, and it is difficult to guarantee the accuracy of the extracted features in the current increasingly complex electromagnetic environment. Target recognition accuracy, robustness to scene and perception device changes, recognition response speed and adaptive ability when new targets appear

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