Radar signals classification using energy‐time‐frequency distribution features

Seddighi, Zahra; Ahmadzadeh, Mohammad Reza; Taban, Mohammad Reza

doi:10.1049/iet-rsn.2019.0331

Cited by 20 publications

(9 citation statements)

References 20 publications

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“…After choosing an appropriate segmenting method, we examine different classifiers in several circumstances. We choose nine common classifiers to recognize emitter identification, containing kernel support vector machine (KSVM) [20], probabilistic neural Fig. 7 Plots of moments for various emitters network (PNN) [12], k-nearest neighbor (KNN) [14], discriminant analysis classifier (DAC) [21], and their variants.…”

Section: Classificationmentioning

confidence: 99%

A novel framework for extracting moment-based fingerprint features in specific emitter identification

Zhao

Wang

Sun

et al. 2023

EURASIP J. Adv. Signal Process.

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Extensive experiments illustrate that moments and their derivations can act as effective fingerprint features for specific emitter identification. Nevertheless, the lack of mechanistic explanation restricts the moment-based fingerprint features to a trial-based and data-driven technique. To make up for theoretical weakness and enhance generalization ability, we analytically investigate how intentional modulation and unintentional modulation affect moments. A framework for extracting moment-based fingerprint features is proposed through fine-segmenting slices. Fingerprint features are extracted, followed by segmenting signals into a combination of sinewaves and calculating their moments. The proposed framework shows advantages in mechanism interpretability and generalizing ability. Simulations and experiments verified the correctness and effectiveness of the proposed framework.

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Section: Classificationmentioning

confidence: 99%

A novel framework for extracting moment-based fingerprint features in specific emitter identification

Zhao

Wang

Sun

et al. 2023

EURASIP J. Adv. Signal Process.

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“…The normalized difference description index values of fingerprint features under different target numbers of radiation sources I (I=3, 5,10,15,20) is shown in Table 9, where X represents the original IP feature, Y 0 is the enlarged feature without correlation calculation, and Y is the enlarged UMME feature calculated by the proposed method.…”

Section: Overall Change Of Featuresmentioning

confidence: 99%

“…ASD, also known as "local wave decomposition", has been widely applied in several research fields [27,28]. For RFF problems, ASD is usually used to directly decompose the time-domain waveform of individual signals and further extract new fingerprint features rather than distinguishing UIM [14,15,22]. Liang et al [29] used empirical mode decomposition to obtain stray components.…”

Section: Introductionmentioning

confidence: 99%

Unintentional modulation microstructure enlargement

Sun

Wang

Huang

2022

J. of Syst. Eng. Electron.

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Radio frequency fingerprinting (RFF) is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level, device-specific imperfections. The RFF-related information is mainly in the form of unintentional modulation (UIM), which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation (IM). It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF. This paper proposes a UIM microstructure enlargement (UMME) method based on featurelevel adaptive signal decomposition (ASD), accompanied by autocorrelation and cross-correlation analysis. The common IM part is evaluated by analyzing a newly-defined benchmark feature. Three different indexes are used to quantify the similarity, distance, and dependency of the RFF features from different devices. Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode. The visual image qualitatively shows the magnification of feature differences; different indicators quantitatively describe the changes in features. Compared with the original RFF feature, recognition results based on the Gaussian mixture model (GMM) classifier further validate the effectiveness of the proposed algorithm.

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“…The computed covariance matrices are used as polarimetric-contextual features. They are given to a support vector machine (SVM) classifier with a matrix logarithm-based kernel [24,25]. Finally, a guided filter [26] is applied to the initial classification map to remove the noisy pixels and achieve a smoothed classification map by preserving the discontinuities and edges.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Kernel‐based covariance descriptor for classification of polarimetric synthetic aperture radar images

Imani

2021

IET Radar Sonar & Navi

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There are two types of important information in a polarimetric synthetic aperture radar (PolSAR) image: spatial features in two dimensions and polarimetric characteristics in the scattering dimension. Considering both polarimetric and spatial information is important for PolSAR image classification. Convolutional kernels show superior performance for extraction of spatial information from two dimensions of an image in convolutional neural networks (CNNs). But learning CNNs needs large enough training sets to achieve the optimum weights of kernels while there are not usually sufficient training samples for PolSAR images. To deal with this difficulty, a convolutional kernel‐based covariance descriptor (CKCD) is introduced for PolSAR image classification in this study. To extract contextual characteristics, compatible with the original image, the fixed‐valued convolutional kernels randomly selected from the image are used, which do not require any learning, and so do not need any training samples. To include more local spatial information and find the relation among the polarimetric features, the covariance descriptor is constructed on the extracted feature maps. Then, the polarimetric‐contextual features are given to a support vector machine with a matrix logarithm‐based kernel. Finally, the guided filter is applied to the initial classification map to result a smoothed classification map with preserved edges. The experiments on three real PolSAR images show superiority of the proposed CKCD method compared to several PolSAR classification methods such as 2DCNN and 3DCNN in small sample size situations.

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Radar signals classification using energy‐time‐frequency distribution features

Cited by 20 publications

References 20 publications

A novel framework for extracting moment-based fingerprint features in specific emitter identification

A novel framework for extracting moment-based fingerprint features in specific emitter identification

Unintentional modulation microstructure enlargement

Convolutional Kernel‐based covariance descriptor for classification of polarimetric synthetic aperture radar images

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