On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets

Persico, Adriano Rosario; Clemente, Carmine; Gaglione, Domenico; Ilioudis, Christos; Cao, Jianlin; Pallotta, Luca; Maio, Antonio De; Proudler, Ian K.; Soraghan, John J.

doi:10.1109/taes.2017.2665258

Cited by 91 publications

(88 citation statements)

References 22 publications

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“…Figure 1 shows the block diagram of the classification method in [9] with which the proposed method shares the principal steps. The received signal s rx (n), n = 0, ..., N , with N the number of available samples, is pre-processed before the extraction of the microDoppler information.…”

Section: Feature Extraction Algorithmmentioning

confidence: 99%

“…The classification performances of the extracted feature vectors are evaluated using a modified version of the kNearest Neighbour (kNN) classifier [9]. The choice of the kNN classifier is justified for its capability to give as output the scores for each class and for its low computational load.…”

Section: Feature Extraction Algorithmmentioning

confidence: 99%

“…The micro-Doppler signature has been used in [8] to extract feature based on the pseudo-Zernike moments for target classification. This approach has been also used successfully to recognize ballistic threats in [9], in which other two different feature extraction approaches are analysed: the 2D (2-dimensional) Gabor filters based features [10] and the Average Cadence Velocity Diagram (ACVD) based features. These three techniques are based on the processing of the Cadence Velocity Diagram (CVD) which represents the cadence of each signal frequency component.…”

Section: Introductionmentioning

confidence: 99%

“…The Kr-moments based approach is compared with the Gabor filter approach which outperforms the other techniques proposed in [9]. The remainder of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Micro-Doppler classification of ballistic threats using Krawtchouk moments

Persico

Clemente

Pallotta

et al. 2016

2016 IEEE Radar Conference (RadarConf)

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This version is available at https://strathprints.strath.ac.uk/56501/ 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. Micro-Doppler Classification of Ballistic Threats Using Krawtchouk MomentsAdriano Rosario Persico * , Carmine Clemente * , Luca Pallotta † , Antonio De Maio ‡ and John Soraghan * * University of Strathclyde, CESIP, EEE, 204, George Street, G1 1XW, Glasgow, UK E-mail: adriano.persico, carmine.clemente, j.soraghan-@strath.ac.uk † CNIT, viale G.P. Usberti, n. 181/A -43124 Parma, c/o udr Università "Federico II", via Claudio 21, I-80125 Napoli, Italy. E-mail: luca.pallotta@unina.it. ‡ Università degli Studi di Napoli "Federico II", Dipartimento di Ingegneria Elettrica e delle Tecnologie dellInformazione, Via Claudio 21, I-80125 Napoli, Italy. E-mail: ademaio@unina.it.Abstract-The challenge of ballistic missiles classification is getting greater importance in last years. In fact, since the antimissile defence systems have generally a limited number of interceptors, it is important to distinguish between warheads and confusing objects that the missile releases during its flight, in order to maximize the interception success ratio. For this aim, a novel micro-Doppler based classification technique is presented in this paper characterized by the employment of Krawtchouk moments. Since the evaluation of the latter requires a low computational time, the proposed approach is suitable for real time applications. Finally, a comparison with the 2-dimensional Gabor filter based approach is described by testing both the techniques on real radar data.

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Section: Feature Extraction Algorithmmentioning

confidence: 99%

Section: Feature Extraction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The Kr-moments based approach is compared with the Gabor filter approach which outperforms the other techniques proposed in [9]. The remainder of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Micro-Doppler classification of ballistic threats using Krawtchouk moments

Persico

Clemente

Pallotta

et al. 2016

2016 IEEE Radar Conference (RadarConf)

Self Cite

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“…Several micro-motion feature extraction algorithms have been presented [7][8][9][10][11][12] . The autocorrelation in time domain and spectrum in frequency domain are utilized to extract the period of micro-Doppler variation 7 . A method to estimate the top angle of the tumbling cone is proposed 8 .…”

Section: Introductionmentioning

confidence: 99%

Precession feature extraction of ballistic missile warhead with high velocity

Sun¹

2018

AIP Conference Proceedings

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Abstract. This paper establishes the precession model of ballistic missile warhead, and derives the formulas of microDoppler frequency induced by the target with precession. In order to obtain micro-Doppler feature of ballistic missile warhead with precession, micro-Doppler bandwidth estimation algorithm, which avoids velocity compensation, is presented based on high-resolution time-frequency transform. The results of computer simulations confirm the effectiveness of the proposed method even with low signal-to-noise ratio.

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Signal preprocessing routines for the detection and classification of human micro‐Doppler radar signatures

Tekir

Yılmaz

Özdemir

2023

Micro & Optical Tech Letters

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In this study, we introduce a radar sensor that we have developed for the micro-Doppler signature measurements together with three consecutive signal preprocessing routines that are to be used for primary detection and classification of human gait and movements. The developed C-band radar sensor has been used in a series of human walking and running experiments that were conducted at Mersin University facilities. First, the raw data collected in time domain are transformed to two-dimensional (2D) timefrequency plane to investigate the Doppler characteristics of human movements. To detect and extract the main body signature of the human, a Kalman filter-based torso signature detection methodology is introduced as the first preprocessing tool. Then, to be able to better detect the other body parts' signatures, the torso's response is thoroughly extracted from the 2D time-Doppler image as the second preprocessing routine by applying a circular shift procedure along each time-bin. As the last procedure, envelopes of the micro-Doppler signatures on the 2D time-Doppler image plane are determined by another routine based on human Doppler threshold method. To assess the success and the validity of the proposed pre-signal processing tools, they are applied to the measured data collected via different experiments of human movements. After applying all these steps, the torso signature and envelopes of all other body parts' signatures are marked and determined with good fidelity.

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On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets

Cited by 91 publications

References 22 publications

Micro-Doppler classification of ballistic threats using Krawtchouk moments

Micro-Doppler classification of ballistic threats using Krawtchouk moments

Precession feature extraction of ballistic missile warhead with high velocity

Signal preprocessing routines for the detection and classification of human micro‐Doppler radar signatures

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