Robust PCA micro-doppler classification using SVM on embedded systems

Zabalza, Jaime; Clemente, Carmine; Caterina, Gaetano Di; Ren, Jinchang; Soraghan, John J.; Marshall, Stephen

doi:10.1109/taes.2014.130082

Cited by 91 publications

(57 citation statements)

References 24 publications

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“…It has been shown how features extracted from the Short Time Fourier Transform (STFT) of these signatures can be used to classify human targets from animals and vehicles in a ground surveillance radar context [6,7], to discriminate between different activities performed by people such as walking, running, crawling [8][9][10][11][12][13], and even to identify specific individuals performing the same activity by exploiting the characteristic walking gait and small movement patterns that each individual exhibits [14][15][16]. Time-frequency transforms [4] other than STFTs have been also proposed to characterise micro-Doppler signatures, such as the Gabor transform, Wigner-Ville transform, Cohen's class timefrequency distributions [17] or Empirical Mode Decomposition [18,19], all of which have been shown to be effective in representing minute movements [18].…”

Section: Introductionmentioning

confidence: 99%

Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar

Fioranelli

Ritchie

Gürbüz

et al. 2017

IEEE Trans. Aerosp. Electron. Syst.

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Feature diversity for optimized human micro-doppler classification using multistatic radar. IEEE Transactions on Aerospace and Electronic Systems, 53(2), pp. 640-654. (doi:10.1109/TAES.2017.2651678) This is the author's final accepted version.There may be differences between this version and the published version. FEATURE DIVERSITY FOR OPTIMIZED HUMAN MICRO-DOPPLER CLASSIFICATION USING MULTISTATIC RADARFrancesco Fioranelli (1) , Matthew Ritchie (2) , Sevgi Zübeyde Gürbüz (3) , Hugh Griffiths (2) ( AbstractThis paper investigates the selection of different combinations of features at different multistatic radar nodes, depending on scenario parameters, such as aspect angle to the target and signal-to-noise ratio, and radar parameters, such as dwell time, polarisation, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analysed for two separate problems, namely the classification of unarmed vs potentially armed multiple personnel, and the personnel recognition of individuals based on walking gait. The results show that the overall classification accuracy can be significantly improved by taking into account feature diversity at each radar node depending on the environmental parameters and target behaviour, in comparison with the conventional approach of selecting the same features for all nodes.

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Section: Introductionmentioning

confidence: 99%

Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar

Fioranelli

Ritchie

Gürbüz

et al. 2017

IEEE Trans. Aerosp. Electron. Syst.

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“…Bearing that in mind, SVM has been proven to be very robust and adequate in multi-class classification [6,20,22]. Additionally, the wide use of SVM in recent years has led to many and easy-to-use libraries even for embedded implementations [23,24]. Hence, SVM is employed as classifier for supervised learning in our experiments, using the LIBSVM library available in [25] that offers a user-friendly interface with Matlab environment.…”

Section: Data Classificationmentioning

confidence: 99%

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Zabalza

Qing

Yuen

et al. 2018

Journal of the Franklin Institute

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This version is available at https://strathprints.strath.ac.uk/60870/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.

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“…On the other hand, for both SSA and F-SSA, several combinations of window L and EVG, as summarized in Table I, are selected to evaluate the corresponding performance. Once the corresponding features are extracted, they are inputted to an SVM for data classification, as SVM is widely used in HSI [4][5] and remote sensing even in embedded systems [30][31]. LibSVM library [32] with Gaussian RBF kernel [4][5]19] is used here, with penalty c and gamma parameters optimally determined through a grid search.…”

Section: B Experimental Settingsmentioning

confidence: 99%

Fast Implementation of Singular Spectrum Analysis for Effective Feature Extraction in Hyperspectral Imaging

Zabalza

Ren

Wang

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This version is available at https://strathprints.strath.ac.uk/53416/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 Abstract-As a recent approach for time series analysis, singular spectrum analysis (SSA) has been successfully applied for feature extraction in hyperspectral imaging (HSI), leading to increased accuracy in pixel-based classification tasks. However, one of the main drawbacks of conventional SSA in HSI is the extremely high computational complexity, where each pixel requires individual and complete singular value decomposition (SVD) analysis. To address this issue, a fast implementation of SSA (F-SSA) is proposed for efficient feature extraction in HSI. Rather than applying pixel-based SVD as conventional SSA does, the fast implementation only needs one SVD applied to a representative pixel, i.e. either the median or the mean spectral vector of the HSI hypercube. The result of SVD is employed as a unique transform matrix for all the pixels within the hypercube. As demonstrated in experiments using two well-known publicly available data sets, almost identical results are produced by the fast implementation in terms of accuracy of data classification, using the support vector machine (SVM) classifier. However, the overall computational complexity has been significantly reduced.

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Robust PCA micro-doppler classification using SVM on embedded systems

Cited by 91 publications

References 24 publications

Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar

Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Fast Implementation of Singular Spectrum Analysis for Effective Feature Extraction in Hyperspectral Imaging

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