Radar emitter classification based on unidimensional convolutional neural network

Sun, Jun; Xu, Guangluan; Ren, Wenjuan; Yan, Zhiyuan

doi:10.1049/iet-rsn.2017.0547

Cited by 51 publications

(33 citation statements)

References 14 publications

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“…For the radar emitter identification task, deep learning models can often achieve the best results. Therefore, in this section, we construct the CNN model [6] and the U-CNN model [7] to compare with our method proposed in this paper. In the two deep learning models, radar pulse description words are used to represent radar signals, and as input to the model, which is the same as the processing of our method, so it is appropriate to compare CNN, U-CNN and our method together.…”

Section: Results Discussionmentioning

confidence: 99%

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An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

et al. 2019

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Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine (SVM) model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then, we design a semi-supervised co-training algorithm using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Simulation experiments show that the proposed method can effectively identify an unknown radar emitter and still maintain high identification accuracy within a certain measurement error range.

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Section: Results Discussionmentioning

confidence: 99%

“…Cain L et al [6] investigated an application of convolutional neural networks (CNN) for rapid and accurate classification of electronic warfare emitters. Sun J et al [7] proposed a deep learning model named as unidimensional convolutional neural network (U-CNN) to classify the encoded high-dimension sequences with big data.…”

Section: Introductionmentioning

confidence: 99%

An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

et al. 2019

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“…The ever-increasing dimensions This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ of feature vectors lead to more and more classifiers, such as support vector machine (SVM) [1], [7], [8], [11], relevance vector machine (RVM) [12], neural networks [1], [13], and even deep learning networks (e.g., convolutional neural networks (CNN) [14] and recurrent neural networks (RNN) [4]) are introduced to classify the pulses based on the features.…”

Section: Introductionmentioning

confidence: 99%

Online Non-Cooperative Radar Emitter Classification From Evolving and Imbalanced Pulse Streams

Sui

Liu

et al. 2020

IEEE Sensors J.

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Recent research treats radar emitter classification (REC) problems as typical closed-set classification problems, i.e., assuming all radar emitters are cooperative and their pulses can be pre-obtained for training the classifiers. However, such overly ideal assumptions have made it difficult to fit real-world REC problems into such restricted models. In this paper, to achieve online REC in a more realistic way, we convert the online REC problem into dynamically performing subspace clustering on pulse streams. Meanwhile, the pulse streams have evolving and imbalanced properties which are mainly caused by the existence of the non-cooperative emitters. Specifically, a novel data stream clustering (DSC) algorithm, called dynamic improved exemplar-based subspace clustering (DI-ESC), is proposed, which consists of two phases, i.e., initialization and online clustering. First, to achieve subspace clustering on subspace-imbalanced data, a static clustering approach called the improved ESC algorithm (I-ESC) is proposed. Second, based on the subspace clustering results obtained, DI-ESC can process the pulse stream in real-time and can further detect the emitter evolution by the proposed evolution detection strategy. The typically dynamic behavior of emitters such as appearing, disappearing and recurring can be detected and adapted by the DI-ESC. Extinct experiments on real-world emitter data show the sensitivity, effectiveness, and superiority of the proposed I-ESC and DI-ESC algorithms.

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“…When the receiver receives multiple signals, only after the intercepted signals are effectively separated can the subsequent recognition be carried out . It is worth mentioning that the traditional separation technique based on radio frequency, direction Of arrival, time of arrival, pulse repetition interval, and pulse duration is limited by existing knowledge, which has not played much role in complex electromagnetic environment .…”

Section: Introductionmentioning

confidence: 99%

Modulation recognition for radar emitter signals based on convolutional neural network and fusion features

Gao

Shen

Gao

2019

Trans Emerging Tel Tech

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With the increase of radar signal modulations and the emergence of new system radars, the receiver will intercept radar signals at the same time. In order to accurately estimate and suppress the signals, this paper proposes an accurate recognition system for radar emitter signals. The system can effectively separate multiple signals and accurately recognize Binary Phase Shift Keying (BPSK), Linear Frequency Modulation (LFM), Continuous Wave (CW), Costas, Frank code, and P1 to P4 codes. The separation technique based on fractional Fourier transform is proposed to decompose received signals into multiple components.Furthermore, a transferable GoogleNet is explored to achieve accurate recognition of the first component with better separation effect. Meanwhile, variational mode decomposition is developed to eliminate the noise of the second component; then, the fusion features are extracted to improve the recognition rate of the second component. Finally, the improved particle swarm optimization algorithm is proposed to find best support vector machine parameters. The simulation results show that the recognition rate of single signal and double signals can reach 96.23% and 72%, respectively, when signal-to-noise ratio is 0 dB.The system can also bring some inspiration to medical and mechanical signal recognition.Trans Emerging Tel Tech. 2019;30:e3612.wileyonlinelibrary.com/journal/ett

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Radar emitter classification based on unidimensional convolutional neural network

Cited by 51 publications

References 14 publications

An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

Online Non-Cooperative Radar Emitter Classification From Evolving and Imbalanced Pulse Streams

Modulation recognition for radar emitter signals based on convolutional neural network and fusion features

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