Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-24T05:51:36.261Z Has data issue: false hasContentIssue false
  • English
  • Français

Radar micro-Doppler mini-UAV classification using spectrograms and cepstrograms

Published online by Cambridge University Press:  08 June 2015

Ronny I.A. Harmanny ,
Jacco J.M. de Wit  and
Gilles Premel-Cabic
Ronny I.A. Harmanny*
Affiliation:
Thales Nederland B.V., Sensors, Advanced Development, Delft, The Netherlands. Phone: +31 15 251 78 29
Jacco J.M. de Wit
Affiliation:
TNO, Department of Radar Technology, The Hague, The Netherlands
Gilles Premel-Cabic
Affiliation:
Thales Nederland B.V., Sensors, Advanced Development, Delft, The Netherlands. Phone: +31 15 251 78 29
*
Corresponding author: R.I.A. Harmanny Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The radar micro-Doppler signature of a target is determined by parts of the target moving or rotating in addition to the main body motion. The relative motion of these parts is characteristic for different classes of targets, e.g. the flapping motion of a bird's wings versus the spinning of propeller blades. In the present study, the micro-Doppler signature is exploited to discriminate birds and small unmanned aerial vehicles (UAVs). Emphasis is on micro-Doppler features that can be extracted from spectrograms and cepstrograms, enabling the human eye or indeed automatic classification algorithms to make a quick distinction between man-made objects and bio-life. In addition, in case of man-made objects, it is desired to further characterize the type of mini-UAV to aid the threat assessment. Also this characterization is done on the basis of micro-Doppler features.

Keywords

Radar signal processing and system modelingRadar applications
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 7 , Special Issue 3-4: European Microwave Week 2014 , June 2015 , pp. 469 - 477
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1] Chen, V.: The Micro-Doppler Effect in Radar, Artech House, Norwood, MA, 2011.Google Scholar
[2] Pennycuick, C.J.: Wingbeat frequency of birds in steady cruising flight: new data and improved predictions. J. Exp. Biol., 199 (1996), 1613–1618.Google Scholar
[3] Bogert, B.P.; Healy, M.J.R.; Tukey, J.W.: The Quefrency Alanysis of Time Series for Echoes: Cepstrum, Pseudo Autocovariance, Cross-Cepstrum and Saphe Cracking, in Rosenblatt, M. (ed.), Proc. of the Symp. on Time Series Analysis, Wiley, New York, 1963. Chapter 15, p. 209–243.Google Scholar
[4] Noll, A.M.; Schroeder, M.R.: Short-time ‘Cepstrum’ pitch detection. in (abstract), J. Acoust. Soc. Am., 36 (5), 1030.Google Scholar
[5] de Wit, J.J.M.; Harmanny, R.I.A.; Prémel-Cabic, G.: Micro-Doppler analysis of small UAV, in Proc. EuRAD, Amsterdam, The Netherlands, October 31–November 2, 2012.Google Scholar
[6] van der Heijden, F.; Duin, R.P.W.; de Ridder, D.; Tax, D.M.J.: Classification, Parameter Estimation and State Estimation, Wiley & Sons, Chichester, UK, 2004.CrossRefGoogle Scholar