Electromagnetic signal classification method and device

An electromagnetic signal and signal technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of unsatisfactory anti-noise performance and high computational complexity, achieve efficient and accurate electromagnetic signal classification, reduce computational complexity, Overcome the effect of noise interference

Pending Publication Date: 2020-06-02
36TH RES INST OF CETC
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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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  • Electromagnetic signal classification method and device
  • Electromagnetic signal classification method and device
  • Electromagnetic signal classification method and device

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Embodiment Construction

[0022] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0023] In 2012, Mallat et al., inspired by the deep convolutional neural network, proposed a convolutional network model with strict mathematical theory support and excellent feature extraction capabilities: wavelet scattering convolutional neural network. This network structure is very similar to the deep convolutional neural network, but its filter is a pre-set wavelet filter, which does not need to be learned. The features obtained translati...

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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...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/06G06F2218/08G06F18/2135G06F18/2411
Inventor 周华吉徐杰郑仕链杨小牛
Owner 36TH RES INST OF CETC
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