Deep Neural Network-Based Interrupted Sampling Deceptive Jamming Countermeasure Method

With the development of digital radio frequency memory technology, the main-lobe deception jamming represented by interrupted-sampling repeater jamming (ISRJ) poses a severe challenge to radar. Traditional anti-jamming methods usually need to estimate the jamming parameters and have the risk of losi...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2022, Vol.15, p.1-12
Main Authors: Lv, Qinzhe, Quan, Yinghui, Sha, Minghui, Feng, Wei, Xing, Mengdao
Format: Article
Language:English
Subjects:
Anti-jamming
Antijamming
Artificial neural networks
Convolution
convolutional neural network (CNN)
Convolutional neural networks
Datasets
Detection
Dimensions
Echoes
Feature extraction
Fourier transforms
Jamming
jamming recognition
Mathematical models
Methods
Neural networks
Object detection
Parameters
Radar
Radar clutter
Radar echoes
Radar imaging
Sampling
Target detection
Time-frequency analysis
Velocity
Velocity measurement
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Summary:With the development of digital radio frequency memory technology, the main-lobe deception jamming represented by interrupted-sampling repeater jamming (ISRJ) poses a severe challenge to radar. Traditional anti-jamming methods usually need to estimate the jamming parameters and have the risk of losing target information. For the above problems, this article proposes a deep neural network-based ISRJ recognition and anti-jamming target detection method which consists of four serial steps. Firstly, the proposed method obtains the time-frequency image set of radar echoes by short-time Fourier transform (STFT). Secondly, a you-only-look-once (YOLO) model is used to detect the jammed echoes, and the positioning result is automatically corrected to avoid losing the target information. Thirdly, the anti-ISRJ target ranging and velocity measurement datasets are constructed according to the positioning result. Finally, an anti-ISRJ target detection model based on the convolution neural network (CNN) is designed to extract features along different dimensions and obtain the range and velocity of the real targets. Experiments on simulated and measured datasets show that the proposed method has better anti-jamming detection performance than the traditional method, and doesn't need to estimate the jamming parameters.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2022.3214969