Cumulants-based Radar Specific Emitter Identification

Aubry, Augusto; Bazzoni, A.; Carotenuto, Vincenzo; Maio, Antonio De; Failla, Pierluigi

doi:10.1109/wifs.2011.6123155

Cited by 41 publications

(44 citation statements)

References 9 publications

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“…As a result of averaging out the received functional according to (11), Ed(u) value is received according to (12). …”

Section: Multi-channel Recognition System With An Independent Distancmentioning

confidence: 99%

“…This causes that ) ( B V d distance of the B V vector from G class, defined according to (11), is modified to the form of (20), where p is the number of non-zero eigenvalues of covariance matrix, (19) This causes that d(V B ) distance of the V B vector from G class, defined according to (11), is modified to the form of (20), where p is the number of non-zero eigenvalues of co- …”

Section: The Process Of Learningmentioning

confidence: 99%

“…his causes that ) ( B V d distance of the B V vector from G lass, defined according to (11), is modified to the form of 20), where p is the number of non-zero eigenvalues of ovariance matrix, base for classification of the tested radar to an appropriate class. In order to compare the received results of RER method with other methods described in works [7,12,13,15,16,17,32], Correct Identification Coefficient (CIC) is set according to (22), where Table 4.…”

Section: The Process Of Learningmentioning

confidence: 99%

“…distance of the B V vector from G ss, defined according to (11), is modified to the form of ), where p is the number of non-zero eigenvalues of variance matrix, …”

Section: Is Causes Thatmentioning

confidence: 99%

“…Defining classes is not only setting the expected average values of basic measurable parameters of a radar signal, i.e., radio frequency (RF), pulse width (PW) and pulse repetition interval (PRI). This process also requires defining variance and correlation of these features [11]. The second significant element of the classification pro-J.…”

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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“…As a result of averaging out the received functional according to (11), Ed(u) value is received according to (12). …”

Section: Multi-channel Recognition System With An Independent Distancmentioning

confidence: 99%

Section: The Process Of Learningmentioning

confidence: 99%

Section: The Process Of Learningmentioning

confidence: 99%

“…distance of the B V vector from G ss, defined according to (11), is modified to the form of ), where p is the number of non-zero eigenvalues of variance matrix, …”

Section: Is Causes Thatmentioning

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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A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

Abdel‐Moneim

El‐Shafai

Abdel-Salam

et al. 2021

Int J Communication

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Automatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency-selective multipath fading, and time-varying channels in real-world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood-based (LB) and feature-based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal-to-noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC. K E Y W O R D S automatic modulation classification (AMC), deep learning (DL), feature-based (FB) methods, likelihood-based (LB) methods | INTRODUCTIONAutomatic modulation classification (AMC) is important in wireless communication systems used in military and civilian applications to enhance the efficiency of the spectrum utilization, redue the overhead, and resolve the shortage problems. Unfortunately, the restricted spectrum resources barely satisfy the ever-increasing demand for 5G 1,2 and Internet of Things (IoT) networks. 3 The AMC can be used for better management of the available spectrum. A simple block diagram of a communication system based on AMC is presented in Figure 1. 4 The AMC architecture contains two steps: signal preprocessing and a proper algorithm for classification. The preprocessing tasks involve reduction of noise, carrier frequency estimation, symbol period estimation, equalization, and signal power evaluation. On the other hand, the AMC methods comprise traditional methods including decisiontheoretic methods and feature-based methods 4,5 along with advanced methods 6 as shown in Figure 2.

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An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

Abdel‐Moneim

Al‐Makhlasawy

Bauomy

et al. 2022

Trans Emerging Tel Tech

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Recently, automatic modulation classification (AMC) has extensively and commonly been utilized in several modern wireless communication systems as a significant tool of signal detection for civilian and military applications and cognitive radio systems. Although several methods have been established to identify the modulation scheme of a received signal, they show a difficulty of learning radio characteristics for most conventional machine learning algorithms. This article focuses on the deep learning (DL) classification technique to solve these problems. To improve the classification accuracy of a communication signal modulation type, we apply a new model that combines Gabor filtering and thresholding with the help of convolution filters implemented in DL. A basic convolutional neural network, AlexNet, and a residual neural network are used for being compatible with constellation diagrams in order to achieve a superior classification performance. Moreover, the Gabor filter can effectively extract spatial information, including edges and textures. In terms of classification accuracy, the proposed AMC system improves the signal modulation classification accuracy significantly, and achieves competitive results. We use seven modulation types over the range of signal-noise ratio (SNR) values from −10 to 30 dB. The performed experiments reveal that the proposal guarantees a remarkable classification accuracy of approximately 100% at a 10 dB SNR over AWGN and Rayleigh fading channels. Therefore, to prove the functional viability of our proposed method, it can be applied in adaptive modulators that can be used in many devices in applications such as Internet-of-Things (IoT). INTRODUCTIONDue to the increasingly growing demand for wireless radio spectrum bandwidth, better approaches for utilization of the radio spectrum are essential. 1 The automatic modulation classification (AMC) is one of the most common techniques for identifying frequency spectra, spectrum management, electronic warfare, cognitive radio networks, interference

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Cumulants-based Radar Specific Emitter Identification

Cited by 41 publications

References 9 publications

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

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

A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

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