Aerospace and Electronic Systems Magazine August 2016 - 40
MACHe - Model-Based Algorithm for Classification of Helicopters
[4]
[5]
[6]
[7]
[8]
[9]
[10]
Figure 2.
Performance on simulated data in terms of percentage of correct classification, for each target on varying the SNR.
ACKNOWLEDGMENTS
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1 and
the MOD University Defence Research Collaboration in Signal
Processing.
REFERENCES
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Chen, V. The Micro-Doppler Effect in Radar.Norwood, MA: Artech
House, 2011.
Clemente, C., Balleri, A., Woodbridge, K., and Soraghan, J. J. Developments in target micro-doppler signatures analysis: radar imaging,
ultrasound and through-the-wall radar, EURASIP Journal on Advances in Signal Processing, vol. 2013, no. 1, pp. 1-18, 2013.
Clemente, C., Pallotta, L., De Maio, A., Soraghan, J. J., and Farina, A.
A Novel Algorithm for Radar Classification Based on Doppler Characteristics Exploiting Orthogonal Pseudo-Zernike Polynomials, IEEE
Transactions on Aerospace and Electronic Systems, vol. 51, no. 1, pp.
417-430, January 2015.
[11]
Martin, J. and Mulgrew, B. Analysis of the theoretical radar return
signal form aircraft propeller blades, Record of the 1990 IEEE International Radar Conference, May 1990, pp. 569-572.
Rotander, C. E. and Von Sydow, H. Classification of helicopters by the
L/N-quotient, in Proceedings of the Radar 97 (Conference Publication 449), Oct 1997, pp. 629-633.
Sang-Ho, Y., Byungwook, K., and Young-Soo, K. Helicopter classification using time-frequency analysis, Electronics Letters, vol. 36, no.
22, pp. 1871-1872, Oct 2000.
Lei, Z., Ming, L., and Xiaowei, Z. Helicopter classification with a
high PRF radar," in 2012 International Conference on Systems and
Informatics (ICSAI), May 2012, pp. 1682-1686.
D. Gaglione, D., C. Clemente, C., F. Coutts, F., L. Gang, L., and J.
J. Soraghan, J. J. Model-based sparse recovery method for automatic
classification of helicopters, in 2015 IEEE International Radar Conference, May 2015, pp. 1161-1165.
Duda, R. O., Hart, R. E., and Stork, D. G. Pattern Classification.
Hoboken, NJ: Wiley, 2012.
Gang, L., and Varshney, P. K. Micro-Doppler parameter estimation
via parametric sparse representation and pruned orthogonal matching
pursuit, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 12, pp. 4937-4948, Dec 2014.
Prouty, R., Helicopter Aerodynamics Volume I. Raleigh, NC: Lulu.
com, 2009, vol. 1.
Domenico Gaglione received
his BSc and MSc degrees (with
honours) in Telecommunications Engineering from Universitá degli Studi di Napoli "Federico II", Italy, in 2011 and
2013, respectively. He is currently a PhD candidate involved
in the UDRC phase II project at
the Centre for Signal and Image
Processing (CeSIP), University
of Strathclyde, Glasgow, UK,
working on novel signal processing methods and algorithms
for Automatic Target Recognition (ATR) and tracking from radar,
in Distributed MIMO Radar Systems (DMRSs) and Synthetic
Aperture Radar (SAR) systems. He was the recipient of the First
Prize at the Student Paper Competition of the 2015 IEEE International Radar Conference, Arlington, VA, USA.
His research interests include micro-Doppler based and SAR
based classification and identification, Compressive Sensing based
radar techniques and MIMO radar signal processing.
IEEE A&E SYSTEMS MAGAZINE
AUGUST 2016
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