Hybrid radar emitter recognition based on rough k-means classifier and SVM

Wu, Zhilu; Yang, Zhutian; Sun, Hongjian; Yin, Zhendong; Nallanathan, Arumugam

doi:10.1186/1687-6180-2012-198

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…The approaches proposed by Wang, L. et al . [ 27 ], Zhang, G. et al [ 25 ] and Wu, Z. et al [ 26 ] are for comparison. We note that the CLAC feature is adopted for these approaches in comparison.…”

Section: Resultsmentioning

confidence: 99%

“…In [ 25 ], a radar emitter recognition approach is proposed by using a classifier combining a minimum Mahalanobis distance classifier and SVM, which can recognize the numbers of intra-pulse modulation radar emitter signals. In [ 26 ], a hybrid radar emitter recognition is proposed. In [ 27 ], a robust radar emitter recognition based on fuzzy theory is proposed, which can recognize radar emitters robustly to some extent.…”

Section: Introductionmentioning

confidence: 99%

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Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

Yang

Qiu

Sun

et al. 2016

Sensors

Self Cite

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Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

Yang

Qiu

Sun

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The widely-used traditional classifier and deep learning network are the typical examples. In [ 19 , 20 ], a clustering algorithm was used to help SVM extract features, but it could not identify complex signal styles. Since deep neural networks are widely used in radar recognition, the authors of [ 21 , 22 , 23 , 24 ] used a denoising auto-encoder (DAE), a convolutional neural network (CNN), a residual neural network (ResNet), and a recurrent neural network (RNN), respectively, to achieve their recognition rates of more than 90%, under the given conditions, which verified the advantages of the deep learning method in the field of signal recognition, but at the same time, it also exposed the weakness of a single network facing a complex environment.…”

Section: Related Workmentioning

confidence: 99%

Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework

Pan

Xiong

Zhuo

2023

Sensors

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Mode recognition is a basic task to interpret the behavior of multi-functional radar. The existing methods need to train complex and huge neural networks to improve the recognition ability, and it is difficult to deal with the mismatch between the training set and the test set. In this paper, a learning framework based on residual neural network (ResNet) and support vector machine (SVM) is designed, to solve the problem of mode recognition for non-specific radar, called multi-source joint recognition framework (MSJR). The key idea of the framework is to embed the prior knowledge of radar mode into the machine learning model, and combine the manual intervention and automatic extraction of features. The model can purposefully learn the feature representation of the signal on the working mode, which weakens the impact brought by the mismatch between training and test data. In order to solve the problem of difficult recognition under signal defect conditions, a two-stage cascade training method is designed, to give full play to the data representation ability of ResNet and the high-dimensional feature classification ability of SVM. Experiments show that the average recognition rate of the proposed model, with embedded radar knowledge, is improved by 33.7% compared with the purely data-driven model. Compared with other similar state-of-the-art reported models, such as AlexNet, VGGNet, LeNet, ResNet, and ConvNet, the recognition rate is increased by 12%. Under the condition of 0–35% leaky pulses in the independent test set, MSJR still has a recognition rate of more than 90%, which also proves its effectiveness and robustness in the recognition of unknown signals with similar semantic characteristics.

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“…K-means clustering algorithm is the most classical and widely used partition clustering algorithm. Wu [11] proposed a radar pulse recognition system based on support vector machine and K-means clustering in order to sort in high density and complex environment. In this method, K-means clustering algorithm is used to sort radar signals, which used Euclidean distance to describe the correlation between pulses, and a more compact clustering boundary can be generated according to the distribution of data sets.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Radar Signal Sorting Based on K-means Clustering and Cuckoo Search Methods

Deng

Kong

Jiang

et al. 2023

Preprint

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In this paper, a fusion clustering radar signal sorting algorithm is proposed to overcome the shortcoming of the traditional K-means clustering sorting algorithm, which is sensitive to the initial clustering center and easy to fall into a local optimal solution. The improved algorithm combines K-means clustering algorithm and cuckoo search, to reduce the dependence on the initial clustering center and avoid falling into local optimum. Further, a new definition method of fitness function and a new adjustment mechanism of discovery probability are proposed, and sine and cosine guidance are introduced in the process of location updating. The number of clusters is proposed to be adjusted adaptively according to the distance between classes, and the initial cluster center is determined by the data field. Implementation results show that the sorting accuracy of the new algorithm is 96.6%, which is 54% higher than that of the traditional K-means clustering. Compared with PSO-Kmeans, the accuracy is improved by 22%, and the convergence time is reduced by 10 seconds on average.

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Hybrid radar emitter recognition based on rough k-means classifier and SVM

Cited by 8 publications

References 18 publications

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework

Implementation of Radar Signal Sorting Based on K-means Clustering and Cuckoo Search Methods

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