Electromagnetic signal classification method and device

Abstract

The embodiment of the invention discloses an electromagnetic signal classification method and a device. The method comprises the following steps: constructing a scattering network through wavelet scattering transformation; scattering features of electromagnetic signals in the electromagnetic signal set are extracted by using the constructed scattering network; and obtaining a feature sample set, training a support vector machine (SVM) classification model by using the feature sample set, extracting scattering features of electromagnetic signals to be classified by using the constructed scattering network, and inputting the extracted scattering features into the trained SVM classification model to obtain a classification result. According to the method of the invention, the scattering network and the support vector machine are organically combined, the structure of the convolutional neural network is reserved, and a filter obtained through data learning in the convolutional neural network is replaced by the pre-constructed wavelet filter, so that the calculation complexity is greatly reduced; through wavelet cascade operation, noise interference in the signal classification processcan be effectively overcome, and efficient and accurate electromagnetic signal classification is achieved.

Description

technical field [0001] The invention relates to the field of electromagnetic signal classification, in particular to an electromagnetic signal classification method and device. Background technique [0002] Electromagnetic signal recognition, in layman's terms, is to distinguish various electromagnetic signals as much as possible by mining the characteristics of electromagnetic signals. Electromagnetic signal identification includes electromagnetic signal type identification and individual radiation source identification, etc. It has a wide range of applications in electromagnetic spectrum monitoring, cognitive radio, cyberspace security and other fields. [0003] Electromagnetic signal recognition mainly adopts pattern recognition method. The artificial neural network has strong adaptive ability, but under the condition of low signal-to-noise ratio, the classification effect is not good. At the same time, because the characteristics of the description object need to be tra...

AI Technical Summary

Problems solved by technology

However, the anti-noise performance of the electromagnetic signal classification method based on the convolutional neural network is not ideal, and the filter in the convolutional neural network is obtained through a large amount of data learning, and the computational complexity is still high.

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