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 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. 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.