Radar interference signal identification method based on deep CNN integration

Abstract

The invention discloses a radar interference signal identification method based on deep CNN (Convolutional Neural Network) integration, belongs to the field of radar signal identification, and aims tosolve the problems of low identification accuracy and weak robustness of an identification system caused by overfitting and poor model generalization capability of an existing deep learning model foridentifying radar signals. The radar interference signal identification method comprises: a first step of dividing a radar interference signal time domain data set into a training set, a verificationset and a test set; a second step of performing replaced random sampling on the training set X for T times to obtain T mutually independent sampling training sets; a third step of adopting a one-dimensional CNN as a feature extractor and adopting a support vector machine as a classifier to construct individual learners, and training T individual learners according to the T sampling training setsin the second step to construct homogeneous integration and construct a model; and a fourth step of inputting a to-be-detected radar interference signal into the model in the third step for identification.

Description

technical field [0001] The invention belongs to the field of radar signal identification, and relates to a technology for identifying radar signals by using a convolutional neural network. Background technique [0002] With the continuous improvement of the level of science and technology, electronic warfare has become an important means of combat in modern warfare. In the increasingly complex electromagnetic environment of the battlefield, the anti-interference ability of radar has also become the key to the victory or defeat of the war. Ground identification and classification are the foundation and key of radar anti-jamming technology. The key step in the identification process of radar jamming signals is the extraction of characteristic parameters. However, with the rapid development of modern militarization technology, the forms of radar jamming signals are becoming more and more complex. If you continue to rely on manual experience to extract artificial features, it wi...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of over-fitting and poor model generalization ability in the recognition of radar signals using the existing deep learning model, resulting in low recognition accuracy and weak robustness of the recognition system, and provides a deep convolutional neural network Network-Integrated Radar Interference Signal Recognition Method

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