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Classification of small UAVs and birds by micro-Doppler signatures

Published online by Cambridge University Press:  19 March 2014

Pavlo Molchanov ,
Ronny I.A. Harmanny ,
Jaco J.M. de Wit ,
Karen Egiazarian  and
Jaakko Astola
Pavlo Molchanov*
Affiliation:
Department of Signal Processing, Tampere University of Technology, Tampere, Finland
Ronny I.A. Harmanny
Affiliation:
Thales Nederland B.V., Delft, The Netherlands
Jaco J.M. de Wit
Affiliation:
Department of Radar Technology, TNO, The Hague, The Netherlands
Karen Egiazarian
Affiliation:
Department of Signal Processing, Tampere University of Technology, Tampere, Finland
Jaakko Astola
Affiliation:
Department of Signal Processing, Tampere University of Technology, Tampere, Finland
*
Corresponding author: P. Molchanov Email: pavlo.molchanov@tut.fi
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Abstract

The popularity of small unmanned aerial vehicles (UAVs) is increasing. Therefore, the importance of security systems able to detect and classify them is increasing as well. In this paper, we propose a new approach for UAVs classification using continuous wave radar or high pulse repetition frequency (PRF) pulse radars. We consider all steps of processing required to make a decision out of the raw radar data. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Then, classification features are extracted from the micro-Doppler signature in order to represent information about class at a lower dimension space. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with X-band radar. Planes, quadrocopter, helicopters, and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides the capability of correct classification with a probability of around 92%.

Keywords

Radar applicationsRadar signal processing and system modeling
Type
Research Paper
Information
International Journal of Microwave and Wireless Technologies , Volume 6 , Issue 3-4: European Microwave Week 2013 , June 2014 , pp. 435 - 444
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2014 

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References

REFERENCES

[1]Toscano, M.: Unmanned aircraft systems roadmap to the future, in 7th North Dakota Research Corridor UAS Summit, May 2013, 569–572.Google Scholar
[2]Koolhoven, M.: Ratelband drone plane crash at Binnenhof. http://www.telegraaf.nl/binnenland/20876587/__Ratelband_laat_vliegtuigje_crashen__.html, September 2013.Google Scholar
[3]RT, German ‘Pirates’ stage mini-drone stunt at Merkel rally. http://rt.com/news/pirates-drone-stunt-merkel-953/, September 2013.Google Scholar
[4]Skolnik, M.: Introduction to Radar Systems, New York, McGraw-Hill, 1962.Google Scholar
[5]Clemente, C.; Balleri, A.; Woodbridge, K.; Soraghan, J.: Developments in target micro-doppler signatures analysis: radar imaging ultrasound and through-the-wall radar. EURASIP J. Adv. Signal Process., 2013 (1) (2013), 47.Google Scholar
[6]Chen, V.: The Micro-Doppler Effect in Radar, Artech House, London, 2011.Google Scholar
[7]de Wit, J.J.M.; Harmanny, R.I.A.; Prémel-Cabic, G.: Micro-Doppler analysis of small UAVs, in Radar Conf. (EuRAD), 2012 European, Amsterdam, The Netherlands, October 2012, 210–213.Google Scholar
[8]Bullard, B.; Dowdy, P.: Pulse doppler signature of a rotary-wing aircraft. Aerosp. Electron. Syst. Mag., IEEE, 6 (5) (1991), 28–30.Google Scholar
[9]Chen, V.; Ling, H.: Time-Frequency Transforms for Radar Imaging and Signal Analysis, Boston, MA, Artech House Radar Library, Artech House, 2002.Google Scholar
[10]Yoon, S.-H.; Kim, B.; Kim, Y.-S.: Helicopter classification using time-frequency analysis. Electron. Lett., 36 (22) (2000), 1871–1872.Google Scholar
[11]Cilliers, A.; Nel, W.A.J.: Helicopter parameter extraction using joint time-frequency and tomographic techniques, in 2008 Int. Conf. on Radar, Adelaide, SA, 2008, 598–603.CrossRefGoogle Scholar
[12]Setlur, P.; Ahmad, F.; Amin, M.: Helicopter radar return analysis: estimation and blade number selection. Signal Process., 91 (2011), 1409–1424.Google Scholar
[13]Thayaparan, T.; Abrol, S.; Riseborough, E.; Stankovic, L.; Lamothe, D.; Duff, G.: Analysis of radar micro-Doppler signatures from experimental helicopter and human data. Radar, Sonar Navig., IET, 1 (2007), 289–299.Google Scholar
[14]Park, J.; Lim, H.; Myung, N.: Analysis of jet engine modulation effect with extended Hilbert-Huang transform. Electron. Lett., 49 (3) (2013), 215–216.Google Scholar
[15]Vaseghi, S.V.: Advanced Digital Signal Processing and Noise Reduction, Chichester, UK, John Wiley & Sons, 2006.Google Scholar
[16]Duda, R.O.; Hart, P.E.; Stork, D.G.: Pattern Classification, 2nd ed., New York, NY, Wiley, 2001.Google Scholar
[17]Cortes, C.; Vapnik, V.: Support-vector network. Mach. Learn., 20 (1995), 273–297.Google Scholar
[18]Wu, T.; Lin, C.; Weng, R.: Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res., 5 (2003), 975–1005.Google Scholar