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Self-similarity matrix based slow-time feature extraction for human target in high-resolution radar

Published online by Cambridge University Press:  25 March 2014

Yuan He Open the ORCID record for Yuan He [Opens in a new window] ,
Pascal Aubry ,
Francois Le Chevalier Open the ORCID record for Francois Le Chevalier [Opens in a new window]  and
Alexander Yarovoy
Yuan He*
Affiliation:
Microwave sensing, signals and systems (MS3), Delft University of Technology, Delft, The Netherlands. Phone: +31 15 2788378
Pascal Aubry
Affiliation:
Microwave sensing, signals and systems (MS3), Delft University of Technology, Delft, The Netherlands. Phone: +31 15 2788378
Francois Le Chevalier
Affiliation:
Microwave sensing, signals and systems (MS3), Delft University of Technology, Delft, The Netherlands. Phone: +31 15 2788378
Alexander Yarovoy
Affiliation:
Microwave sensing, signals and systems (MS3), Delft University of Technology, Delft, The Netherlands. Phone: +31 15 2788378
*
Corresponding author: Y. He Email: [email protected]
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Abstract

A new approach is proposed to extract the slow-time feature of human motion in high-resolution radars. The approach is based on the self-similarity matrix (SSM) of the radar signals. The Mutual Information is used as a measure of similarity. The SSMs of different radar signals (high-resolution range profile, micro-Doppler, and range-Doppler video sequence) are compared, and the angel-invariant property of the SSMs is demonstrated. The SSM for different activities (i.e. walking and running) is extracted from range-Doppler video sequence and analyzed. Finally, simulation result is validated by experimental data.

Keywords

Self-similarity matrixSlow-time featureHuman target analysisHigh-resolution radar
Type
Research Paper
Information
International Journal of Microwave and Wireless Technologies , Volume 6 , Issue 3-4: European Microwave Week 2013 , June 2014 , pp. 423 - 434
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2014 

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References

REFERENCES

[1]Ram, S.S.; Christianson, C.; Youngwook, K.; Ling, H.: Simulation and analysis of human Micro-Dopplers in Through-Wall environments. IEEE Transact. Geosci. Remote Sens., 48 (2010), 2015–2023.CrossRefGoogle Scholar
[2]Vignaud, L.; Ghaleb, A.; Le Kernec, J.; Nicolas, J.M.: Radar high resolution range and micro-Doppler analysis of human motions, in International Radar Conf., France, 2009.Google Scholar
[3]Fogle, O.R.; Rigling, B.D.: Micro-range/micro-Doppler feature extraction and association, in IEEE Radar Conf., USA, 2011.CrossRefGoogle Scholar
[4]Chen, V.C.: Analysis of radar micro-Doppler with time-frequency transform, in Tenth IEEE Workshop on Statistical Signal and Array Processing, USA, 2000.Google Scholar
[5]Chen, V.C.: The Micro-Doppler Effect in Radar, Artech House, Norwood, MA, USA, 2011.Google Scholar
[6]Cutler, R.; Davis, L.: View-based detection and analysis of periodic motion, in Fourteenth Int. Conf. Pattern Recognition, Australia, 1998.Google Scholar
[7]BenAbdelkader, C.; Cutler, R.; Davis, L.: Gait recognition using image self-similarity. EURASIP J. Appl. Signal Process., 4 (2004) 572–585.Google Scholar
[8]Junejo, I.N.; Dexter, E.; Laptev, I.; Perez, P.: View-independent action recognition from temporal self-similarities. IEEE Transact. Pattern Anal. Mach. Intell., 33 (2011), 172–185.Google Scholar
[9]He, Y.; Le Chevalier, F.; Yarovoy, A.G.: Association of range-doppler video sequences in multistatic UWB radar for human tracking, in European Radar Conf., Netherlands, 2012.Google Scholar
[10]Van Dorp, P.; Groen, F.C.A.: Human walking estimation with radar. IEE Proc. Radar, Sonar Navig., 150 (2003), 356–365.Google Scholar
[11]Motion capture database. http://mocap.cs.cmu.edu/.Google Scholar
[12]Eckmann, J.P.; Kamphorst, S.O.; Ruelle, D.: Recurrence plots of dynamical systems. Europhys. Lett., 4 (1987), 973–977.Google Scholar
[13]Cutler, R.; Davis, L.: Robust real-time periodic motion detection, analysis, and applications. IEEE Transact. Pattern Anal. Mach. Intell., 22 (2000), 781–796.CrossRefGoogle Scholar
[14]Shechtman, E.; Irani, M.: Matching local self-similarities across images and videos, in IEEE Conf. Computer Vision and Pattern Recognition, USA, 2007.Google Scholar
[15]Van der Weken, D.; Nachtegael, M.; Kerre, E.E.: An overview of similarity measures for images, in IEEE Int. Conf. Acoustics, Speech, and Signal Processing, USA, 2002, 3317–3320.Google Scholar
[16]Pluim, J.P.W.; Maintz, J.B.A.; Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Transact. Med. Imaging, 22 (2003), 986–1004.Google Scholar
[17]Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J., 27 (1948), 379–423.Google Scholar
[18]Li, J.: Automatic Classification of Human Motions using Doppler Radar. Master thesis, School of Electrical, Computer, and Telecommunications Engineering, University of Wollongong, 2012.Google Scholar
[19]Molchanov, P.O.; Astola, J.T.; Egiazarian, K.O.; Totsky, A.V.: Target classification by using pattern features extracted from bispectrum-based radar Doppler signatures, in Int. Radar Symp., Germany, 2011.Google Scholar
[20]He, Y.; Savelyev, T.G.; Yarovoy, A.G.: Two-stage algorithm for extended target tracking by multistatic UWB radar. IEEE CIE Int. Conf. Radar, China, 2011.Google Scholar