A Study on Radar Emitter Recognition Based on SPDS Neural Network

Yin, Zhendong; Yang, Wei; Yang, Zhutian; Zuo, Lei; Han-song, Gao

doi:10.3923/itj.2011.883.888

Cited by 17 publications

(13 citation statements)

References 4 publications

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“…In [14] higher order spectral analysis (HOSA) techniques are used to extract information from LPI (low probability of intercept) radar signals and to produce 2D signatures, which are then fed to a NN for detecting and identifying the LPI radar signal. The work presented in [15] investigates the potential of NN (MLP) when used in Forward Scattering Radar (FSR) applications for target classification. The authors analyze collected radar signal data and extract features, which are then used to train NN for target classification.…”

Section: Neural Network In Radar Recognition Systemsmentioning

confidence: 99%

Radar Emitter Signals Recognition and Classification with Feedforward Networks

Petrov

Jordanov

Roe³

2013

Procedia Computer Science

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A possible application of neural networks for timely and reliable recognition of radar signal emitters is investigated. In particular, a large data set of intercepted generic radar signal samples is used for investigating and evaluating several neural network topologies, training parameters, input and output coding and machine learning facilitating data transformations. Three case studies are discussed, where in the first two the radar signals are classified in two broad classes -with civil or military application, based on patterns in their pulse train characteristics and in the third one trained to distinguish between several more specific radar functions. Very competitive results of about 82%, 84% and 67% are achieved on the testing data sets.

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Section: Neural Network In Radar Recognition Systemsmentioning

confidence: 99%

Radar Emitter Signals Recognition and Classification with Feedforward Networks

Petrov

Jordanov

Roe³

2013

Procedia Computer Science

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“…In [8], Zhang et al proposed a method based on the rough sets theory and radial basis function (RBF) neural network. Yin et al proposed a radar emitter recognition method using the single parameter dynamic search neural network [9]. However, the prediction accuracy of the neural network approaches is not high and the application of neural networks requires large training sets, which may be infeasible in practice.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

Yang

Yin

et al. 2013

Sensors

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Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches.

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“…Traditional recognition methods are becoming inefficient against this emerging issue [3]. Many new radar emitter recognition methods were proposed, e.g., intrapulse feature analysis [4], stochastic context-free grammars analysis [1], and artificial intelligence analysis [5][6][7][8]. In particular, the artificial intelligence analysis approach attracted much attention.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Sun

et al. 2012

EURASIP J. Adv. Signal Process.

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Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this article, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, i.e., the primary signal recognition and the advanced signal recognition. In the former step, the rough k-means classifier is proposed to cluster the samples of radar emitter signals by using the rough set theory. In the latter step, the samples within the rough boundary are used to train the support vector machine (SVM). Then SVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and has a lower time complexity than the traditional approaches.

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A Study on Radar Emitter Recognition Based on SPDS Neural Network

Cited by 17 publications

References 4 publications

Radar Emitter Signals Recognition and Classification with Feedforward Networks

Radar Emitter Signals Recognition and Classification with Feedforward Networks

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

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

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