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