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Label consistent K-SVD for sparse micro-doppler classification

Coutts, Fraser K. and Gaglione, Domenico and Clemente, Carmine and Li, Gang and Proudler, Ian K. and Soraghan, John J. (2015) Label consistent K-SVD for sparse micro-doppler classification. In: 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, pp. 90-94. ISBN 978-1-4799-8058-1

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Abstract

Secondary motions of targets observed by radar introduce non-stationary returns containing the so-called micro-Doppler information. This is characterizing information that can be exploited to enhance automatic target recognition systems. In this paper, the challenge of classifying the micro-Doppler return of helicopters is addressed. A robust dictionary learning algorithm, Label Consistent K-SVD (LC-KSVD), is applied to identify effectively and efficiently helicopters. The effectiveness of the proposed algorithm is demonstrated on both synthetic and real radar data.

Author(s):Coutts, Fraser K., Gaglione, Domenico, Clemente, Carmine, Li, Gang, Proudler, Ian K. and Soraghan, John J.
Item type:Book Section
ID code:55571
Keywords:doppler radar, airborne radar, helicopters, radar signal processing, radar target recognition, singular value decomposition, LC-KSVD, automatic target recognition systems, label consistent K-SVD, microdoppler information, microdoppler return, radar data, robust dictionary learning algorithm, secondary motions, sparse microDoppler classification, accuracy, blades, classification algorithms, dictionaries, radar, training, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
Department:Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
Depositing user: Pure Administrator
Date deposited:18 Feb 2016 14:31
Last modified:03 Jan 2019 14:33
URI:https://strathprints.strath.ac.uk/id/eprint/55571
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