Self-Attention Bi-LSTM Networks for Radar Signal Modulation Recognition

Wei, Shunjun; Qu, Qizhe; Zeng, Xiangfeng; Liang, Jiadian; Shi, Jun

doi:10.1109/tmtt.2021.3112199

Cited by 50 publications

(20 citation statements)

References 24 publications

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“…Let k be the serial number of network layers; e be a node on the current layer; ξ be the error matrix of the feature map of the current layer; ε' be the derivative of the activation function; US be the up-sampling operation; ⨁ be the Hadamard product. The error matrix of formula ( 25) can be obtained by combining formulas ( 26)- (28). For each convolutional layer:…”

Section: Radar Signal Recognition Algorithmmentioning

confidence: 99%

“…Specifically, the features of the given 1D signal series are extracted directly by the 1D convolutional layer, and weighed according to their importance to the recognition by the attention mechanism. Wei et al [28] constructed a new network based on end-to-end series, and used the network to recognize the eight kinds of pulse modulation for radar signals. The network is composed of a shallow CNN, an attention-based bidirectional long shortterm memory (LSTM) network, and a dense neural network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Liu¹,

Antypenko²,

Sushko³

et al. 2021

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Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.

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Section: Radar Signal Recognition Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Liu¹,

Antypenko²,

Sushko³

et al. 2021

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show abstract

“…In [13], a framework based on the capsule network has been proposed to solve the small sample modulation-type detection problem. In [14], a sequence-based network has been proposed. In [15], a convolutional neural network for radio modulation classification has been presented, which is not only suitable for the complex time domain of radio signals but also can achieve excellent results under low signal-to-noise ratio (SNR) conditions.…”

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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“…Deep learning has been applied to the field of radar signal recognition [9][10][11]. However, designing and training a deep convolutional neural network (CNN) from scratch requires sufficient hardware resources and a large amount of training time.…”

Section: Introductionmentioning

confidence: 99%

Multi‐label hybrid radar signal recognition based on a feature pyramid network and class activation mapping

Luo

Deng

2022

IET Radar Sonar & Navi

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The electromagnetic environment of modern battlefields becomes increasingly complex, and radar receivers may receive multiple radar signals simultaneously. However, current deep learning models can only predict a single class and cannot recognize multi‐label mixed radar signals. In this study, a multi‐label hybrid radar signal recognition framework based on the feature pyramid network (FPN) and class activation map (CAM) is proposed. The multi‐label radar signals are recognized by calculating the average value of the CAM corresponding to each class. The proposed method can recognize, localize and separate mixed radar signals in time‐frequency images, which improves the interpretability and transparency of the model. In addition, the FPN is adopted to improve the spatial resolution of the feature maps, and the Mixup data augmentation is utilized to improve the generalization performance of the model. Experiments with eight different modulation types of mixed radar signals show that the recognition accuracy of hybrid radar signals achieves 92.2% at 0 dB.

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Self-Attention Bi-LSTM Networks for Radar Signal Modulation Recognition

Cited by 50 publications

References 24 publications

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

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

Multi‐label hybrid radar signal recognition based on a feature pyramid network and class activation mapping

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