Fast Complex-Valued CNN for Radar Jamming Signal Recognition

Zhang, Haoyu; Yu, Lei; Chen, Yushi; Yan, Wei

doi:10.3390/rs13152867

Cited by 22 publications

(12 citation statements)

References 28 publications

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“…Although these deep-learning methods have achieved excellent performance in other fields, there is very little literature [27,28] on deep-learning-based radar ISRJ suppression. Accordingly, based on the analysis of ISRJ characteristics, combined with the existing ISRJ suppression methods and deep-learning methods, this paper proposes a multi-level and multi-domain joint anti-jamming deep network (MSMD network) with excellent performance.…”

Section: Introductionmentioning

confidence: 99%

Interrupted-Sampling Repeater Jamming-Suppression Method Based on a Multi-Stages Multi-Domains Joint Anti-Jamming Depth Network

Liao

Peng

et al. 2022

Remote Sensing

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Jamming will seriously affect the detection ability of radar, so it is essential to suppress the jamming of radar echoes. Interrupted-sampling repeater jamming (ISRJ) based on a digital-radio-frequency-memory (DRFM) device can generate false targets at the victim radar by the interception and repeating of the radar transmission signal, which is highly correlated with the true target signal. ISRJ can achieve main lobe jamming and has both deception and oppressive jamming effects, so it is difficult for the existing methods to suppress this jamming effectively. In this paper, we propose a deep-learning-based anti-jamming network, named MSMD-net (Multi-stage Multi-domain joint anti-jamming depth network), for suppressing ISRJ main lobe jamming in the radar echo. In the first stage of MSMD-net, considering that the target signal is difficult to detect under a high jamming-to-signal ratio (JSR), we propose a preprocessing method of limiting filtering on the time-frequency domain to reduce the JSR using the auxiliary knowledge of radar. In the second stage, taking advantage of the discontinuity of the jamming in the time domain, we propose a UT-net network that combines the U-net structure and the transformer module. The UT-net performs target feature extraction and signal reconstruction in the signal time-frequency domain and preliminarily realizes the suppression of the jamming component. In the third stage, combined with phase information, a one-dimensional complex residual convolution U-net network (ResCU-net) is constructed in the time domain to realize jamming filtering and signal recovery further. The experimental results show that MSMD-net can obtain the best jamming suppression effect under different transmitted signals, different jamming modes, and different jamming parameters.

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Section: Introductionmentioning

confidence: 99%

Interrupted-Sampling Repeater Jamming-Suppression Method Based on a Multi-Stages Multi-Domains Joint Anti-Jamming Depth Network

Liao

Peng

et al. 2022

Remote Sensing

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“…Existing methods based on deep learning have recently achieved remarkable success in the field of signal classification. Many correlative algorithms [10,11] have been proposed and have achieved excellent classification results. For instance, in [12], a graph convolutional modulation recognition framework has been proposed to identify the modulation type of the signal.…”

Section: Introductionmentioning

confidence: 99%

“…To solve the task of modulation signal classification, AlexNet and GoogleNet have been used in [18] and achieved great results, proving the effectiveness of deep learning methods in this area. In [10], the authors proposed a complex-valued convolutional neural network, which was used to study the intrinsic properties of the radar interference signal. The authors in [19] have developed a small sample signal modulation recognition framework using an attention relation network.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Signal Classification Based on Class Exemplar Selection and Multi-Objective Linear Programming

et al. 2022

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In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing electromagnetic signal classification (ESC) models cannot handle new signal types. In this context, the emergence of class-incremental learning aims to incrementally update the classification model as new categories emerge. In this paper, an electromagnetic signal classification framework based on class exemplar selection and a multi-objective linear programming classifier (CES-MOLPC) is proposed in order to continuously learn new classes in an incremental manner. Specifically, our approach involves the adaptive selection of class exemplars considering normalized mutual information and a multi-objective linear programming classifier. The former is used to maintain the classification capability of the model for previous categories by selecting key samples, while the latter is used to allow the model to adapt quickly to new categories. Meanwhile, a weighted loss function based on cross-entropy and distillation loss is presented in order to fine-tune the model. We demonstrate the effectiveness of the proposed CES-MOLPC method through extensive experiments on the public RML2016.04c data set and the large-scale real-world ACARS signal data set. The results of the comparative experiments demonstrate that our method can achieve significant improvements over state-of-the-art methods.

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“…In recent years, with the success and continuous development of deep learning, researchers converted radar signals into time‐frequency images (TFIs) through time‐frequency analysis and used convolutional neural networks to automatically extract features and classify radar signals by supervised learning [3]. The radar signal recognition based on deep learning significantly improves the robustness and recognition accuracy [8–12]. Hoang et al.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, with the success and continuous development of deep learning, researchers converted radar signals into time-frequency images (TFIs) through timefrequency analysis and used convolutional neural networks to automatically extract features and classify radar signals by supervised learning [3]. The radar signal recognition based on deep learning significantly improves the robustness and recognition accuracy [8][9][10][11][12]. Hoang et al [13] presented an LPI radar waveform recognition technology based on a singleshot multi-box detector (SSD) and a supplementary classifier, which demonstrated excellent classification performance for LPI radar waveforms.…”

Section: Introductionmentioning

confidence: 99%

New classes inference, few‐shot learning and continual learning for radar signal recognition

Luo

Deng

2022

IET Radar Sonar & Navi

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Automatic radar modulation recognition plays a significant role in both civilian and military applications. With the rapid development of deep learning, convolutional neural networks have achieved demonstrated success in radar signal recognition. However, the convolutional neural networks usually only recognise trained classes, and when the dataset changes, the networks need to be retrained. However, in actual radar signal recognition applications, the model usually needs to predict new radar signals, and the size of the training set will continue to accumulate. Therefore, few-shot learning and rapid training on dynamic datasets become crucial. In this study, a lifelong learning system based on imprint few-shot learning and Net2Net knowledge transfer for radar signal recognition is proposed. The proposed algorithm adapts to the constant changes of the dataset, which can achieve new classes inference, few-shot learning, and knowledge transfer. The model is trained on the dataset containing 8 types of radar signals and achieves high recognition accuracy in the test dataset containing 12 types of radar signals. The recognition accuracy of the proposed algorithm achieves 91.8% at −2 dB. In addition, Net2Net knowledge transfer can improve the training efficiency on new datasets avoiding training from scratch.

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Fast Complex-Valued CNN for Radar Jamming Signal Recognition

Cited by 22 publications

References 28 publications

Interrupted-Sampling Repeater Jamming-Suppression Method Based on a Multi-Stages Multi-Domains Joint Anti-Jamming Depth Network

Interrupted-Sampling Repeater Jamming-Suppression Method Based on a Multi-Stages Multi-Domains Joint Anti-Jamming Depth Network

Electromagnetic Signal Classification Based on Class Exemplar Selection and Multi-Objective Linear Programming

New classes inference, few‐shot learning and continual learning for radar signal recognition

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