volume | PIER Journals

Abstract

The use of radar micro-Doppler (m-D) signatures for human activities classification, surveillance and healthcare has become a hot topic in recent years. While m-D signals are always multicomponent, it is necessary to separate them into mono-components signals associated with individual body parts for easier features analysis and extraction. In this paper, a novel method called local time-frequency sparse reconstruction (LTFSR) is proposed to iteratively extract and separate m-D components one by one in a descending intensity order from a time-frequency (T-F) representation. For the current strongest m-D component, we first estimate its instantaneous frequency (IF) by dividing the signal into short overlapping time intervals and selecting the best matching chirp atom to approximate the local frequency in each time interval based on matching pursuit. Then, a T-F filtering is used to extract and remove the strongest component from the multicomponent signal. Repeat the above steps until all m-D components are separated. Simulations are given to validate the effectiveness and robustness of the proposed method.

1. Chen, V. C., "Doppler signatures of radar backscattering from objects with micro-motions," IET Signal Processing, Vol. 2, No. 3, 291-300, 2008.
doi:10.1049/iet-spr:20070137 Google Scholar

2. Narayanan, R. M. and M. Zenaldin, "Radar micro-Doppler signatures of various human activities," IET Radar, Sonar and Navigation, Vol. 9, No. 9, 1205-1215, 2015.
doi:10.1049/iet-rsn.2015.0173 Google Scholar

3. Kim, Y. and H. Ling, "Human activity classification based on micro-Doppler signatures using a support vector machine," IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 5, 1328-1337, 2009.
doi:10.1109/TGRS.2009.2012849 Google Scholar

4. Ricci, R. and A. Balleri, "Recognition of humans based on radar micro-Doppler shape spectrum features," IET Radar, Sonar and Navigation, Vol. 9, No. 9, 1216-1223, 2015.
doi:10.1049/iet-rsn.2014.0551 Google Scholar

5. Du, H., T. Jin, Y. Song, and Y. Dai, "Unsupervised adversarial domain adaptation for micro-Doppler based human activity classification," IEEE Geoscience and Remote Sensing Letters, Vol. 17, No. 1, 62-66, 2019.
doi:10.1109/LGRS.2019.2917301 Google Scholar

6. Kim, Y. and T. Moon, "Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks," IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 1, 8-12, 2015.
doi:10.1109/LGRS.2015.2491329 Google Scholar

7. Park, J., R. Javier, T. Moon, and Y. Kim, "Micro-Doppler based classification of human aquatic activities via transfer learning of convolutional neural networks," Sensors, Vol. 16, No. 12, 1990, 2016.
doi:10.3390/s16121990 Google Scholar

8. Li, X., Y. He, and X. Jing, "A survey of deep learning-based human activity recognition in radar," Remote Sensing, Vol. 11, No. 9, 1068, 2019.
doi:10.3390/rs11091068 Google Scholar

9. Seifert, A.-K., A. M. Zoubir, and M. G. Amin, "Radar classification of human gait abnormality based on sum-of-harmonics analysis," 2018 IEEE Radar Conference (RadarConf18), 0940-0945, IEEE, 2018.
doi:10.1109/RADAR.2018.8378687 Google Scholar

10. Seifert, A.-K., M. Amin, and A. M. Zoubir, "Toward unobtrusive in-home gait analysis based on radar micro-Doppler signatures," IEEE Transactions on Biomedical Engineering, Vol. 66, No. 9, 2629-2640, 2019.
doi:10.1109/TBME.2019.2893528 Google Scholar

11. Bjorklund, S., H. Petersson, and G. Hendeby, "On distinguishing between human individuals in micro-Doppler signatures," 2013 14th International Radar Symposium (IRS), Vol. 2, 865-870, IEEE, 2013. Google Scholar

12. Zenaldin, M. and R. M. Narayanan, "Features associated with radar micro-Doppler signatures of various human activities," Radar Sensor Technology XIX; and Active and Passive Signatures VI, Vol. 9461, 94611D, International Society for Optics and Photonics, 2015. Google Scholar

13. Cao, P., W. Xia, M. Ye, J. Zhang, and J. Zhou, "Radar-ID: Human identification based on radar micro-Doppler signatures using deep convolutional neural networks," IET Radar, Sonar and Navigation, Vol. 12, No. 7, 729-734, 2018.
doi:10.1049/iet-rsn.2017.0511 Google Scholar

14. Yang, Y., C. Hou, Y. Lang, G. Yue, Y. He, and W. Xiang, "Person identification using micro-Doppler signatures of human motions and UWB radar," IEEE Microwave and Wireless Components Letters, Vol. 29, No. 5, 366-368, 2019.
doi:10.1109/LMWC.2019.2907547 Google Scholar

15. Fogle, O. R. and B. D. Rigling, "Micro-range/micro-Doppler decomposition of human radar signatures," IEEE Transactions on Aerospace and Electronic Systems, Vol. 48, No. 4, 3058-3072, 2012.
doi:10.1109/TAES.2012.6324677 Google Scholar

16. Abdulatif, S., F. Aziz, B. Kleiner, and U. Schneider, "Real-time capable micro-Doppler signature decomposition of walking human limbs," 2017 IEEE Radar Conference (RadarConf), 1093-1098, IEEE, 2017.
doi:10.1109/RADAR.2017.7944367 Google Scholar

17. He, Y., P. Molchanov, T. Sakamoto, P. Aubry, F. Le Chevalier, and A. Yarovoy, "Range-Doppler surface: A tool to analyse human target in ultra-wideband radar," IET Radar, Sonar and Navigation, Vol. 9, No. 9, 1240-1250, 2015.
doi:10.1049/iet-rsn.2015.0065 Google Scholar

18. Ding, Y. and J. Tang, "Micro-Doppler trajectory estimation of pedestrians using a continuous-wave radar," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 9, 5807-5819, 2014.
doi:10.1109/TGRS.2013.2292826 Google Scholar

19. Shi, X., F. Zhou, M. Tao, and Z. Zhang, "Human movements separation based on principal component analysis," IEEE Sensors Journal, Vol. 16, No. 7, 2017-2027, 2015.
doi:10.1109/JSEN.2015.2509185 Google Scholar

20. Quaiyum, F., N. Tran, J. E. Piou, O. Kilic, and A. E. Fathy, "Noncontact human gait analysis and limb joint tracking using Doppler radar," IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, Vol. 3, No. 1, 61-70, 2018.
doi:10.1109/JERM.2018.2881238 Google Scholar

21. Li, W., G. Kuang, and B. Xiong, "Decomposition of multicomponent micro-Doppler signals based on HHT-AMD," Applied Sciences, Vol. 8, No. 10, 1801, 2018.
doi:10.3390/app8101801 Google Scholar

22. Qiao, X., T. Shan, R. Tao, X. Bai, and J. Zhao, "Separation of human micro-Doppler signals based on short-time fractional fourier transform," IEEE Sensors Journal, Vol. 19, No. 24, 12205-12216, 2019.
doi:10.1109/JSEN.2019.2937989 Google Scholar

23. Mallat, S. G. and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Transactions on Signal Processing, Vol. 41, No. 12, 3397-3415, 1993.
doi:10.1109/78.258082 Google Scholar

24. Zhang, H., L. Yu, and G.-S. Xia, "Iterative time-frequency filtering of sinusoidal signals with updated frequency estimation," IEEE Signal Processing Letters, Vol. 23, No. 1, 139-143, 2015.
doi:10.1109/LSP.2015.2504565 Google Scholar

25. Shell, M., "Carnegie mellon university motion capture database,", 2012. Google Scholar

Dr. Zhongfei Ni

Dr. Bin-Ke Huang