Knowledge-based signal processing for radar ESM systems

Roe, J.; Cussons, S.; Feltham, A.

doi:10.1049/ip-f-2.1990.0045

Cited by 19 publications

(15 citation statements)

References 6 publications

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“…(6) Taking into consideration the fact that the base for estimation of the distance of V B vector feature from G class is the difference r = V B ¡ V -B , the next step is to define K-L transform of the estimated difference, according to (7). The decorrelation process of the vector's features' coordinates according to (8) and the normalization process of dispersion of co-ordinates' values according to (9) results in a correlation matrix equal to an identity matrix according to (10 …”

Section: Multi-channel Recognition System With An Independent Distancmentioning

confidence: 99%

“…The decorrelation process of the vector's features' co-ordinates according to (8) and the normalization process of dispersion of co-ordinates' values according to (9) results in a correlation matrix equal to an identity matrix according to (10). The decorrelation process of the vector's features' coordinates according to (8) and the normalization process of dispersion of co-ordinates' values according to (9) results in a correlation matrix equal to an identity matrix according to (10 …”

Section: Multi-channel Recognition System With An Independent Distancmentioning

confidence: 99%

“…Here, it is essential to indicate a very significant fact, namely, in order to set a distinctive radar signal pattern, radar metrics needs to be defined in the measurable feature space of this signal; only after that the pattern vector of such a signal in the database can be designed [1,6,7]. However, what needs to be taken into account is the specificity of such a database for ELINT systems and electronic warfare (EW), [8,9,10]. The specificity of such a database is not the subject of the content of this article.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Multi-channel Recognition System With An Independent Distancmentioning

confidence: 99%

Section: Multi-channel Recognition System With An Independent Distancmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Electronic support measure (ESM) 3,16 receivers play an important role by intercepting signals and measuring their physical parameters. In most of the cases, ESM systems are unable to recognise the different emitters of the same type or class.…”

Section: Introductionmentioning

confidence: 99%

“…The signal parameters 17 considered for emitter classification are frequency, pulse repetition frequency (PRF), pulse width (PW), and antenna scan period (ASP) 1,16 . Sometimes, emitters operate in different frequency bands and multiple PRFs, this will make the identification problem very difficult.…”

Section: Introductionmentioning

confidence: 99%

Electronic Warfare:Issues and Challenges for Emitter Classification

Gupta¹,

Hareesh²,

Mahla³

2011

DSJ

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Electronic warfare (EW) is an important capability that provides advantage to defence forces over their adversaries. Defence forces gather tactical intelligence through EW sensors, which provide the means to counter hostile actions of enemy forces. Functions of an EW system is threat detection and the area surveillance so as to determine the identity of surrounding emitters. Emitter classification system identifies possible threats by analysing intercepted signals. Problem of identifying emitters based on its intercepted signal characteristics is a challenging problem in electronic warfare studies. Major issues and challenges for emitter classification such as drifting of emitter parameters due to aging, operational characteristic of an emitter, i.e., same emitter can operate on multiple bands and multiple pulse repetition frequencies (PRFs) are highlighted. A novel approach based on some well-known statistical methods, e.g., regression analysis, hypothesis testing, and discriminent analysis is proposed. The effectiveness of the proposed approach has been tested over ELINT (Electronic Intelligence) data and illustrated using simulation data. The proposed approach can play a solution for wide variety of problems in emitter classification in electronic warfare studies.

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