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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 [1] [2] [3] 40 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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