© The Institution of Engineering and Technology
Classification of different human activities using multistatic micro-Doppler data and features is considered in this study, focusing on the distinction between unarmed and potentially armed personnel. A database of real radar data with more than 550 recordings from 7 different human subjects has been collected in a series of experiments in the field with a multistatic radar system. Four key features were extracted from the micro-Doppler signature after a short time Fourier transform analysis. The resulting feature vectors were then used as individual, pairs, triplets and all together before inputting to different types of classifiers based on the discriminant analysis method. The performance of different classifiers and different feature combinations is discussed aiming at identifying the most appropriate features for the unarmed against armed personnel classification, as well as the benefit of combining multistatic data rather than using monostatic data only.
References
-
-
1)
-
23. Blacknell, D., Griffiths, H.: ‘Radar automatic target recognition (ATR) and non-cooperative target recognition (NCTR)’ (Institution of Engineering and Technology, 2013).
-
2)
-
2. Al-Ashwal, W.A.: ‘Measurement and modelling of bistatic sea clutter’. Ph.D. Dissertation, University College London, UK, 2011.
-
3)
-
8. Tahmoush, D., Silvious, J.: ‘Radar micro-Doppler for long range front-view gait recognition’. BTAS IEEE Third Int. Conf. on Biometrics: Theory, Applications, and Systems, Washington, USA, September 2009.
-
4)
-
R. Fisher
.
The use of multiple measurements in taxonomic problems.
Ann. Eugenics
,
2 ,
179 -
188
-
5)
-
12. Chen, P.H., Shastry, M.C., Lai, C.P., Narayanan, R.M.: ‘A portable real-time digital noise radar system for through-the-wall imaging’, IEEE Trans. Geosci. Remote Sens., 2012, 50, (10), pp. 4123–4134 (doi: 10.1109/TGRS.2012.2188411).
-
6)
-
16. Yinan, Y., Jiajin, L., Wenxue, Z., Chao, L.: ‘Target classification and pattern recognition using micro-Doppler radar signatures’. SNPD Seventh ACIS Int. Conf. on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, Las Vegas, USA, June 2006.
-
7)
-
5. Chen, V.C.: ‘The micro-Doppler effect in radar’ (Artech House, 2011).
-
8)
-
R.G. Raj ,
V.C. Chen ,
R. Lipps
.
Analysis of radar human gait signatures.
IET Signal Process.
,
3 ,
234 -
244
-
9)
-
12. Orović, I., Stanković, S., Amin, M.: ‘A new approach for classification of human gait based on time–frequency feature representations’, Signal Process., 2011, 91, (6), pp. 1448–1456 (doi: 10.1016/j.sigpro.2010.08.013).
-
10)
-
Y. Kim ,
H. Ling
.
Human activity classification based on micro-Doppler signatures using a support vector machine.
IEEE Trans. Geosci. Remote Sens.
,
1328 -
1337
-
11)
-
1. Derham, T.E., Doughty, S., Woodbridge, K., Baker, C.J.: ‘Design and evaluation of a low-cost multistatic netted radar system’, IET Radar Sonar Navig., 2007, 1, (5), pp. 362–368 (doi: 10.1049/iet-rsn:20060100).
-
12)
-
15. Setlur, P., Amin, M., Ahmad, F.: ‘Urban target classifications using time-frequency micro-Doppler signatures’. ISSPA Nineth Int. Symp. on Signal Processing and Its Applications, United Arab Emirates, February 2007.
-
13)
-
25. Hastie, T., Tibshirani, R., Friedman, J.: ‘The elements of statistical learning: data mining, inference, and prediction’ (Springer, 2009, 2nd edn.).
-
14)
-
21. Bjorklund, S., Petersson, H., Nezirovic, A., Guldogan, M.B., Gustafsson, F.: ‘Millimeter-wave radar micro-Doppler signatures of human motion’. Proc. of IRS Int. Radar Symp., Leipzig, Germany, September 2011.
-
15)
-
V.C. Chen ,
F. Li ,
S.-S. Ho
.
Micro-Doppler effect in radar: phenomenon, model, and simulation study.
IEEE Trans. Aerosp. Electron. Syst.
,
1 ,
2 -
21
-
16)
-
17. Fairchild, D.P., Narayanan, R.M.: ‘Classification of human motions using empirical mode decomposition of human micro-Doppler signatures’, IET Radar Sonar Navig., 2014, 8, (5), pp. 425–434 (doi: 10.1049/iet-rsn.2013.0165).
-
17)
-
11. Mobasseri, B.G., Amin, M.G.: ‘A time-frequency classifier for human gait recognition’. Proc. of Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification, Orlando, USA, April 2009.
-
18)
-
10. Tahmoush, D., Silvious, J.: ‘Remote detection of humans and animals’. AIPRW IEEE Workshop on Applied Imagery Pattern Recognition, Washington, USA, October 2009.
-
19)
-
26. Stove, A.: ‘A Doppler-based target classifier using linear discriminants and principal components’. IET Seminar on High Resolution Imaging and Target Classification, London, UK, November 2006.
-
20)
-
3. Al-Ashwal, W.A., Baker, C.J., Balleri, A., et al: ‘Statistical analysis of simultaneous monostatic and bistatic sea clutter at low grazing angles’, Electron. Lett., 2011, 47, (10), pp. 621–622 (doi: 10.1049/el.2011.0557).
-
21)
-
11. Tivive, F.C., Bouzerdoum, A., Amin, M.G.: ‘A human gait classification method based on radar Doppler spectrograms’, EURASIP J. Adv. Sig. Proc., 2010, 2010, pp. 1–12 (doi: 10.1155/2010/389716).
-
22)
-
19. Balleri, A., Chetty, K., Woodbridge, K.: ‘Classification of personnel targets by acoustic micro-Doppler signatures’, IET Radar Sonar Navig., 2011, 5, (9), pp. 943–951 (doi: 10.1049/iet-rsn.2011.0087).
-
23)
-
22. Chen, V.C., Tahmoush, D., Miceli, W.J.: ‘Radar micro-Doppler signatures: processing and applications’ (Institution of Engineering and Technology, 2014).
-
24)
-
G. Smith ,
K. Woodbridge ,
C.J. Baker ,
H. Griffiths
.
Multistatic micro-Doppler radar signatures of personnel targets.
IET Signal Process.
,
3 ,
224 -
233
-
25)
-
14. Fairchild, D.P., Narayanan, R.M.: ‘Determining human target facing orientation using bistatic radar micro-Doppler signals’. Proc. of SPIE Conf. on Active and Passive Signatures V, Baltimore, USA, May 2014, vol. 9082, pp. 908203-1–308203-9.
-
26)
-
9. Tahmoush, D., Silvious, J.: ‘Radar polarimetry for security applications’. EuRAD European Radar Conf., Paris, France, September 2010.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn.2014.0360
Related content
content/journals/10.1049/iet-rsn.2014.0360
pub_keyword,iet_inspecKeyword,pub_concept
6
6