Radar-Jamming Classification in the Event of Insufficient Samples Using Transfer Learning

Abstract

Radar has played an irreplaceable role in modern warfare. A variety of radar-jamming methods have been applied in recent years, which makes the electromagnetic environment more complex. The classification of radar jamming is critical for electronic counter-countermeasures (ECCM). In the field of signal classification, machine learning-based methods take great effort to find proper features as well as classifiers, and deep learning-based methods depend on large training datasets. For the above reasons, an efficient transfer learning-based method is proposed in this paper. Firstly, one-dimensional radar signals were transformed into time–frequency images (TFIs) using linear and bilinear time–frequency analysis, which is inspired by symmetry theory. Secondly, pretrained AlexNet and SqueezeNet networks were modified to classify the processed TFIs. Finally, performance of this method was evaluated and compared using a simulated data set with nine types of radar-jamming signals. The results demonstrate that our proposed classification method performs well in accuracy and efficiency at a 1% training ratio, which is practical for anti-jamming.

Maintaining Symmetry between Convolutional Neural Network Accuracy and Performance on an Edge TPU with a Focus on Transfer Learning Adjustments

“…In similar work, modified versions of the AlexNet, ResNet50, DenseNet161, and VGG-16 models were used with transfer learning to detect Leukemia in blood smear images [18]. Modified versions of AlexNet and SqueezeNet were used to classify radar jamming signals [19]. In all of these examples, transfer learning is used to reduce the requirements for training data and training time.…”

A Novel Radar Cross-Section Calculation Method Based on the Combination of the Spectral Element Method and the Integral Method

“…A radar is a cutting-edge electronic device that utilizes electromagnetic waves for target detection, employing radio waves to ascertain the spatial location of targets through processes such as the emission, reflection, and scattering of electromagnetic waves. Due to its exceptional ability to penetrate space and minimal susceptibility to weather conditions, radar technology plays a pivotal role in military applications and finds extensive usage in critical civilian sectors [1]. However, with the continuous advancement of electronic information technology and the intense competition between stealth technology and antistealth technology in information warfare, radar systems face increasingly formidable challenges in detection and target identification [2,3].…”

Recognition of Micro-Motion Jamming Based on Complex-Valued Convolutional Neural Network

“…Thanks to the development of artificial intelligence technology, deep learning has been successfully applied to ISAR jamming pattern recognition [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. For instance, Wang et al [ 27 ] implemented the recognition of jamming patterns by CNN for three kinds of jamming, including suppression jamming, multiple false targets jamming, and narrow-pulse jamming.…”