Your browser does not support JavaScript!

Centroid features for classification of armed/unarmed multiple personnel using multistatic human micro-Doppler

Centroid features for classification of armed/unarmed multiple personnel using multistatic human micro-Doppler

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Radar, Sonar & Navigation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study analyses the use of human micro-Doppler signatures collected using a multistatic radar system to identify and classify unarmed and potentially armed personnel walking within a surveillance area. The signatures were recorded in a series of experimental tests and analysed through short time Fourier transform followed by feature extraction and classification. Features based on singular value decomposition and on the centroid of the micro-Doppler signature are proposed and their suitability for armed versus unarmed classification purposes discussed. It is shown that classification accuracy above 95% can be achieved using a single feature. Features based on the centroid of the signatures are shown to be also effective in cases where there are two people walking together in the same direction and at similar speed, and one of them may be armed or not, i.e. for targets not easily separable in range or in Doppler.


    1. 1)
      • 6. Youngwook, K., Sungjae, H., Jihoon, K.: ‘Human detection using Doppler radar based on physical characteristics of targets’, IEEE Geosci. Remote Sens. Lett., 2015, 12, pp. 289293.
    2. 2)
      • 3. Chen, V.C., Tahmoush, D., Miceli, W.J.: ‘Radar micro-Doppler signatures: processing and applications’ (Institution of Engineering and Technology, 2014).
    3. 3)
      • 15. Perassoli, M., Balleri, A., Woodbridge, K.: ‘Measurements and analysis of multistatic and multimodal micro-Doppler signatures for automatic target classification’. IEEE Radar Conf., Cincinnati, OH, USA, 19–23 May 2014, pp. 03240328.
    4. 4)
      • 18. Fioranelli, F., Ritchie, M., Griffiths, H.: ‘Aspect angle dependence and multistatic data fusion for micro-Doppler classification of armed/unarmed personnel’, IET Radar Sonar Navig., 2015, 9, (9), pp. 12311239.
    5. 5)
      • 11. Tahmoush, D., Silvious, J.: ‘Radar micro-Doppler for long range front-view gait recognition’. IEEE Third Int. Conf. on Biometrics: Theory, Applications, and Systems BTAS '09, Washington DC, USA, September, pp. 16.
    6. 6)
      • 5. Tahmoush, D., Silvious, J.: ‘Remote detection of humans and animals’. IEEE Applied Imagery Pattern Recognition Workshop (AIPRW), Washington DC, USA, 14–16 October 2009, pp. 18.
    7. 7)
      • 12. Karabacak, C., Gürbüz, S.Z., Guldogan, M.B., et al: ‘Multi-aspect angle classification of human radar signatures’. Proc. SPIE 8734, Active and Passive Signatures IV, 873408, May 23, 2013.
    8. 8)
      • 21. De Wit, J.J.M., Harmanny, R., et al: ‘Radar micro-Doppler feature extraction using the singular value decomposition’. 2014 Int. Radar Conf., Lille, France, 2014.
    9. 9)
      • 22. Hastie, T., Tibshirani, R., Friedman, J.: ‘The elements of statistical learning: data mining, inference, and prediction’ (Springer, 2009, 2nd edn.).
    10. 10)
      • 1. Chen, V.C.: ‘Doppler signatures of radar backscattering from objects with micro-motions’, IET Signal Process., 2008, 2, pp. 291300.
    11. 11)
      • 16. Smith, G.E., Woodbridge, K., Baker, C.J., et al: ‘Multistatic micro-Doppler radar signatures of personnel targets’, IET Signal Process., 2010, 4, pp. 224233.
    12. 12)
      • 14. Fairchild, D.P., Narayanan, R.M.: ‘Determining human target facing orientation using bistatic radar micro-Doppler signals’. Proc. SPIE 9082, Active and Passive Signatures V, 908203, June 4, 2014.
    13. 13)
      • 4. Tahmoush, D.: ‘Review of micro-Doppler signatures’, IET Radar Sonar Navig., 2015, 9, (9), pp. 11401146.
    14. 14)
      • 23. Balleri, A., Al-Armaghany, A., Griffiths, H., et al: ‘Measurements and analysis of the radar signature of a new wind turbine design at X-band’, IET Radar Sonar Navig., 2013, 7, pp. 170177.
    15. 15)
      • 17. Fioranelli, F., Ritchie, M., Griffiths, H.: ‘Multistatic human micro-Doppler classification of armed/unarmed personnel’, IET Radar Sonar Navig., 2015, 9, (7), pp. 857865.
    16. 16)
      • 7. Youngwook, K., Hao, L.: ‘Human activity classification based on micro-Doppler signatures using a support vector machine’, IEEE Trans. Geosci. Remote Sens., 2009, 47, pp. 13281337.
    17. 17)
      • 8. Cagliyan, B., Gurbuz, S.Z.: ‘Micro-Doppler-based human activity classification using the mote-scale BumbleBee radar’, IEEE Geosci. Remote Sens. Lett., 2015, 12, pp. 21352139.
    18. 18)
      • 19. Fioranelli, F., Ritchie, M., Griffiths, H.: ‘Classification of unarmed/armed personnel using the NetRAD multistatic radar for micro-Doppler and singular value decomposition features’,IEEE Geosci. Remote Sens. Lett., 2015, 12, (9), pp. 19331937.
    19. 19)
      • 9. Ricci, R., Balleri, A.: ‘Recognition of humans based on radar micro-Doppler shape spectrum features’, IET Radar Sonar Navig., 2015, 9, (9), pp. 12161223.
    20. 20)
      • 13. Tekeli, B., Gurbuz, S.Z., Yuksel, M., et al: ‘Classification of human micro-Doppler in a radar network’. 2013 IEEE Radar Conf., Ottawa, Canada, May 2013, pp. 16.
    21. 21)
      • 10. Tahmoush, D., Silvious, J.: ‘Radar microDoppler for security applications: modeling men versus women’. IEEE Antennas and Propagation Society Int. Symp., APSURSI '09, Charleston, SC, USA, 1–5 June 2009, pp. 14.
    22. 22)
      • 2. Raj, R.G., Chen, V.C., Lipps, R.: ‘Analysis of radar human gait signatures’, IET Signal Process., 2010, 4, pp. 234244.
    23. 23)
      • 20. Derham, T.E., Doughty, S., Woodbridge, K., et al: ‘Design and evaluation of a low-cost multistatic netted radar system’, IET Radar Sonar Navig., 2007, 1, pp. 362368.

Related content

This is a required field
Please enter a valid email address