Identification of radar signals using neural network classifier with low-discrepancy optimisation

Petrov, Nedyalko; Jordanov, Ivan; Roe, J.

doi:10.1109/cec.2013.6557890

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…PDW parameters [ 2 ] were extracted quickly through the parameter estimation and signal sorting in mixed signals, which achieved sorting and recognition within the wide range of the signal to noise ratio (SNR). Since the single PDW sequence had limitations in analyzing the modulation characteristics, according to the actual data samples obtained, the time domain 12-dimensional characteristic parameters of the pulse sequence in the were listed in [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Modulation Signal Recognition Based on Information Entropy and Ensemble Learning

Zhang

Jin

et al. 2018

Entropy

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Abstract:In this paper, information entropy and ensemble learning based signal recognition theory and algorithms have been proposed. We have extracted 16 kinds of entropy features out of 9 types of modulated signals. The types of information entropy used are numerous, including Rényi entropy and energy entropy based on S Transform and Generalized S Transform. We have used three feature selection algorithms, including sequence forward selection (SFS), sequence forward floating selection (SFFS) and RELIEF-F to select the optimal feature subset from 16 entropy features. We use five classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), Adaboost, Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting (XGBoost) to classify the original feature set and the feature subsets selected by different feature selection algorithms. The simulation results show that the feature subsets selected by SFS and SFFS algorithms are the best, with a 48% increase in recognition rate over the original feature set when using KNN classifier and a 34% increase when using SVM classifier. For the other three classifiers, the original feature set can achieve the best recognition performance. The XGBoost classifier has the best recognition performance, the overall recognition rate is 97.74% and the recognition rate can reach 82% when the signal to noise ratio (SNR) is −10 dB.

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Section: Introductionmentioning

confidence: 99%

Modulation Signal Recognition Based on Information Entropy and Ensemble Learning

Zhang

Jin

et al. 2018

Entropy

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“…These are based on solving the linear approximation task recurrently, using gradient methods and nonlinear approximation [18], nonlinear approximation of random function [19] and other methods for adaptive regression splines, classification and approximation [20,21]. This is a typical solution for identification systems such as perceptrons or artificial neural network (ANN), e.g., support vector machine networks (SVM) [22] using Widrow-Hoff learning algorithms, Adaline ANN or the method based on back-propagating errors and neural network classifier with low discrepancy optimization [23,24]. Also, the Fourier transform has been widely used in radar signal and image processing.…”

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confidence: 99%

A method of feature selection in the aspect of specific identification of radar signals

Dudczyk

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. This article presents an important task of classification, i.e. mapping surfaces which separate patterns in feature space in the scope of radar emitter recognition (RER) and classification. Assigning a tested radar to a particular class is based on defining its location from the discriminating areas. In order to carry out the classification process, it is necessary to define metrics in the feature space as it is essential to estimate the distance of a classified radar from the centre of the class. The method presented in this article is based on extraction and selection of distinctive features, which can be received in the process of specific emitter identification (SEI) of radar signals, and on the minimum distance classification. The author suggests a RER system which consists of a few independent channels. The task of each channel is to calculate the distance of the tested radar from a given class and finally, set the correct identification coefficient for each recognized radar. Thus, a multichannel system with independent distance measurement is obtained, which makes it possible to recognize particular radar copies. This system is implemented in electronic intelligence (ELINT) system and tested in real battlefield conditions. Key words: radar emitter recognition (RER), specific emitter identification (SEI), minimum distance classification, ELINT system. cedure is the problem to define how to estimate the distance of a tested radar emitter signal from the centre of the class taking into consideration variance and correlation of vector's features. The RER method also provides a solution when the features of radar patterns are not linearly separable. RER method bases on the analysis of basic measurable parameters of the radar signal (such as RF, PW, PRI) as result of which it is possible to extract additional distinctive features. The RER process is called specific emitter identification (SEI). Additionally extracted distinctive features, which are received in the process of RER, may be a product of out-of-band radiation of radar devices [12]. These features may be of fractal type, which is presented in the works [13,14,15]. The received features may also be a product of inter-pulse modulation [16] and intrapulse analysis of a radar signal [17]. Of course, there are more complicated approaches, which offer effective methods for solving the classification task (i.e. mapping separating surfaces). These are based on solving the linear approximation task recurrently, using gradient methods and nonlinear approximation [18], nonlinear approximation of random function [19] and other methods for adaptive regression splines, classification and approximation [20,21]. This is a typical solution for identification systems such as perceptrons or artificial neural network (ANN), e.g., support vector machine networks (SVM) [22] using Widrow-Hoff learning algorithms, Adaline ANN or the method based on back-propagating errors and neural network classifier with low discrepancy optimization [23,24]. Also, the Fourier tr...

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Radar Emitter Recognition in Airborne RWR/ESM Based on Improved K Nearest Neighbor Algorithm

Wang

2014

2014 IEEE International Conference on Computer and Information Technology

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Identification of radar signals using neural network classifier with low-discrepancy optimisation

Cited by 3 publications

References 18 publications

Modulation Signal Recognition Based on Information Entropy and Ensemble Learning

Modulation Signal Recognition Based on Information Entropy and Ensemble Learning

A method of feature selection in the aspect of specific identification of radar signals

Radar Emitter Recognition in Airborne RWR/ESM Based on Improved K Nearest Neighbor Algorithm

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