Deep Neural Network-Based Interrupted Sampling Deceptive Jamming Countermeasure Method

Abstract

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 antijamming 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 antijamming target detection method which consists of four serial steps. First, the proposed method obtains the time-frequency image set of radar echoes by short-time Fourier transform (STFT). Second, a youonly-look-once (YOLO) model is used to detect the jammed echoes, and the positioning result is automatically corrected to avoid losing the target information. Third, 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 antijamming detection performance than the traditional method, and does not need to estimate the jamming parameters.

Deep Learning-Based Active Jamming Suppression for Radar Main Lobe

“…The encoding objective function was derived from analyzing the principle of quadrature phase encoding against intermittent sampling jamming, followed by applying a genetic algorithm to determine the encoding sequence. In addition, Ly et al [6] developed a CNN-based method for ISRJ identification and antijamming target detection. FrFT was applied to counter SMSP jamming in distributed radar due to distinct FM slopes between spectrum dispersive jamming and radar signals [7].…”

LUFFD-YOLO: A Lightweight Model for UAV Remote Sensing Forest Fire Detection Based on Attention Mechanism and Multi-Level Feature Fusion

“…Deep learning [23][24][25][26][27] is a machine learning technology that uses artificial neural networks to mimic the cognitive processes of the human brain. It is used to analyze data, recognize patterns, and make judgments [28]. The ongoing advancement of deep learning has made the identification of forest fires in many areas a central focus of research.…”

Radar Active Jamming Recognition under Open World Setting

“…CNNs can train their parameters using jamming signals, eliminating the need for manual feature extraction and the design of decision trees for classification criteria. As a result, CNNs have been extensively used in the research of classifying and recognizing radar jamming signals [18].…”