Radar interference detection and identification method based on convolutional neural network

A convolutional neural network and radar jamming technology, applied in the field of identification of jamming signal types, can solve problems such as manual selection of generalization ability, and achieve the effect of strong adaptability, good robustness, and guaranteed accuracy

Active Publication Date: 2020-03-27
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0003] In view of the above-mentioned deficiencies in the prior art, the radar interference detection and recognition method based on the convolutional neural network prov

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  • Radar interference detection and identification method based on convolutional neural network
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  • Radar interference detection and identification method based on convolutional neural network

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[0021] The specific embodiments of the present invention are described below to facilitate those skilled in the art to understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Such changes are obvious within the spirit and scope of the present invention as defined and determined by the appended claims, and all inventions and creations utilizing the inventive concept are within the scope of protection.

[0022] refer to figure 1 , figure 1 The flow chart of the radar jamming detection and identification method based on convolutional neural network is shown, such as figure 1 As shown, the method S includes steps S1 to S8.

[0023] In step S1, down-conversion and down-sampling preprocessing is performed on the collected radar signal / simulated radar signal;

[0024] In step S2, a short-time Fourier transform with low frequency re...

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Abstract

The invention discloses a radar interference detection and identification method based on a convolutional neural network. The method comprises the following steps: preprocessing radar signals; performing time domain transformation on the preprocessed signal by adopting short-time Fourier transform; performing constant false alarm detection and interference measurement on the time-frequency image ain sequence to obtain a time parameter and a frequency parameter of an interference signal; extracting an interference signal in the radar signal according to the time parameter; filtering the extracted interference signal by adopting a band-pass filter according to the frequency parameter; carrying out time domain transformation on the filtered signal by adopting short-time Fourier transform toobtain a time-frequency image b, and carrying out normalization processing; smoothing the normalized time-frequency image b by adopting a Wiener filtering algorithm, and then performing adaptive clipping on a smoothing result; scaling the adaptively clipped image by adopting a bicubic interpolation algorithm to obtain identification data; and inputting the identification data into a pre-trained CNN model for identification to obtain the type of the interference signal.

Description

technical field [0001] The invention relates to the identification of interference signal types, in particular to a radar interference detection and identification method based on a convolutional neural network. Background technique [0002] With the emergence and rapid application of digital radio frequency storage (DFRM) technology, the fidelity of radar active interference in modern electronic warfare is higher, and the forms are complex and diverse, which poses a serious threat to the normal operation and survival of radar. Therefore, how to effectively counteract Jamming is an increasingly urgent need for modern radars. The premise of anti-interference is the correct identification of the interference. The traditional method is the interference classification based on feature extraction, but this kind of method requires manual selection of features and the generalization ability is weak. In recent years, many radar jamming identification algorithms have emerged, but th...

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Application Information

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IPC IPC(8): G01S13/00G01S7/02G01S7/36
CPCG01S13/006G01S7/021G01S7/36
Inventor 张伟刘强康慧吴筱诺李浩曹建蜀
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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