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To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutional autoencoders, which can implement the classification of known and unknown patterns. In the supervised train...
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... With JNR ranging from 10 dB to 15 dB, the average recognition rate for eight jamming types reached 88%, outperforming supervised neural networks. Later, Zhang et al. [167] applied the concept of ZSL to propose an open-world recognition method called the RCAE-OWR algorithm. During supervised training, they built a residual convolutional autoencoder network structure to 23 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING extract semantic information solely from the known training set. ...
In the modern warfare with complex and changeable electromagnetic environment, radar jamming is getting more complex and realistic, which poses a serious threat to radar, jamming recognition has become a hot topic in the field of electronic countermeasures. To make effective anti-jamming measures, numerous jamming recognition methods have been proposed. This paper presents a systematic review of jamming recognition for this topic. Specifically, firstly building a system framework for jamming models, including deception jamming, suppression jamming, and smart jamming, thoroughly explaining the operational mechanisms. Then, recognition methods based on traditional machine learning are summarized, and delves into the advantages and disadvantages of feature extraction methods and classifiers. Furthermore, the focus shifts to neural network-based methods, such as shallow neural network methods and deep neural network methods. In particular, restricted sample strategies are also discussed as potential future directions. Finally, concluding on the current status of jamming recognition methods and the prospects for future work are made. This paper provides a reference for the research of radar jamming recognition.
... According to the format of the input radar jamming signal, these CNN-based methods can be divided into onedimensional methods [16,17] and two-dimensional methods [18,19]. For one-dimensional methods, radar jamming echo sequences are directly fed into deep-learning models [20,21]. Unlike one-dimensional methods, for two-dimensional methods, echo sequences are usually transformed into radar jamming images using a short-time Fourier transform. ...
Radar jamming recognition is a key step in electronic countermeasures, and accurate and sufficient labeled samples are essential for supervised learning-based recognition methods. However, in real practice, collected radar jamming samples often have weak labels (i.e., noisy-labeled or unlabeled ones), which degrade recognition performance. Additionally, recognition performance is hindered by limitations in capturing the global features of radar jamming. The Transformer (TR) has advantages in modeling long-range relationships. Therefore, a weakly supervised Transformer is proposed to address the issues of performance degradation under weak supervision. Specifically, complementary label (CL) TR, called RadarCL-TR, is proposed to improve radar jamming recognition accuracy with noisy samples. CL learning and a cleansing module are successively utilized to detect and remove potentially noisy samples. Thus, the adverse influence of noisy samples is mitigated. Additionally, semi-supervised learning (SSL) TR, called RadarSSL-PL-TR, is proposed to boost recognition performance under unlabeled samples via pseudo labels (PLs). Network generalization is improved by training with pseudo-labeling unlabeled samples. Moreover, the RadarSSL-PL-S-TR is proposed to further promote recognition performance, where a selection module identifies reliable pseudo-labeling samples. The experimental results show that the proposed RadarCL-TR and RadarSSL-PL-S-TR outperform comparison methods in recognition accuracy by at least 7.07% and 6.17% with noisy and unlabeled samples, respectively.
