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2019-02-16
Body Gesture Recognition Based on Polarimetric Micro-Doppler Signature and Using Deep Convolutional Neural Network
By
Wenwu Kang,

    Dr. Wenwu Kang

    The Key Labroratory of Microwave Remote Sensing, Chinese Academy of Sciences

    China

    Homepage

Yunhua Zhang,

    Prof. Yunhua Zhang

    CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences

    China

    Homepage

Xiao Dong

    Dr. Xiao Dong

    Center for Space Science and Applied Research, Chinese Academy of Sciences

    China

    Homepage

Progress In Electromagnetics Research M, Vol. 79, 71-80, 2019
doi:10.2528/PIERM18111509 | Google Scholar

Abstract
Body gesture recognition can be applied not only to social security but also to rescue operations. In reality, body gesture can produce unique micro-Doppler signatures (MDSs), which can be used for identification. In this paper, we first acquired the echo signals of four body gestures via a Ka-band dual polarization radar system under different angles and distances. The four gestures are respectively swinging arm up and down, swinging arm left and right, nodding, and shaking head. Then, time-frequency spectrograms were obtained by short-time Fourier transform, from which we can see that different body gestures have different polarimetric MDSs. Finally, we propose to classify four body gestures using the deep convolutional neural network (DCNN) method. It is shown that by combining HH and HV polarizations, about 92.7% recognition rate is achieved while only about 77.5% and 89.3% rates are obtained by using single HH polarization and single HV polarization, respectively.
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Citation
Wenwu Kang, Yunhua Zhang, and Xiao Dong, "Body Gesture Recognition Based on Polarimetric Micro-Doppler Signature and Using Deep Convolutional Neural Network," Progress In Electromagnetics Research M, Vol. 79, 71-80, 2019.
doi:10.2528/PIERM18111509
References

1. Tekeli, B., S. Z. Gurbuz, and M. Yuksel, "Information-theoretic feature selection for human micro-Doppler signature classification," IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 5, 2749-2762, 2016.
doi:10.1109/TGRS.2015.2505409        Google Scholar

2. Garcia-Rubia, J. M., O. Kilic, V. Dang, Q. M. Nguyen, and N. Tran, "Analysis of moving human micro-doppler signature in forest environments," Progress In Electromagnetics Research, Vol. 148, 1-14, 2014.
doi:10.2528/PIER14012306        Google Scholar

3. Chen, V. C., F. Li, S. S. Ho, and H. Wechsler, "Micro-Doppler effect in radar: Phenomenon, model, and simulation study," IEEE Transactions on Aerospace and Electronic Systems, Vol. 42, No. 1, 2-21, 2006.
doi:10.1109/TAES.2006.1603402        Google Scholar

4. Chen, V. C., F. Li, S. S. Ho, and H. Wechsler, "Analysis of micro-Doppler signature," IEE Proceeding - Radar Sonar and Navigation, Vol. 150, No. 4, 271-276, 2003.
doi:10.1049/ip-rsn:20030743        Google Scholar

5. Li, L., Z. Huang, and W. Zhang, "Instantaneous frequency estimation methods of micro-doppler signal," Progress In Electromagnetics Research C, Vol. 58, 125-134, 2015.
doi:10.2528/PIERC15060203        Google Scholar

6. 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

7. Fioranelli, F., M. Ritchie, and H. Griffiths, "Multistatic human micro-Doppler classification of armed/unarmed personnel," IET Radar Sonar and Navigation, Vol. 9, No. 7, 857-865, 2015.
doi:10.1049/iet-rsn.2014.0360        Google Scholar

8. 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, 2016.
doi:10.1109/LGRS.2015.2491329        Google Scholar

9. Kim, Y. and B. Toomajian, "Hand gesture recognition using micro-doppler signatures with convolutional neural network," IEEE Access, Vol. 4, 7125-7130, 2016.
doi:10.1109/ACCESS.2016.2617282        Google Scholar

10. Kim, Y. and Y. Li, "Human activity classification with transmission and reflection coefficients of on-body antennas through deep convolutional neural networks," IEEE Transactions on Antennas and Propagation, Vol. 65, No. 5, 2764-2768, 2017.
doi:10.1109/TAP.2017.2677918        Google Scholar

11. Chen, Z., G. Li, F. Fioranelli, and H. Griffiths, "Personnel recognition and gait classification based on multistatic micro-Doppler signatures using deep convolutional neural networks," IEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 5, 669-673, 2018.
doi:10.1109/LGRS.2018.2806940        Google Scholar

12. 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

13. Kang, W., Y. Zhang, and X. Dong, "Polarimetric MDS of pedestrian," Electronics Letters, Vol. 54, No. 17, 1051-1053, 2018.
doi:10.1049/el.2018.0208        Google Scholar

14. Kim, B. K., H. Kang, and S. Park, "Experimental analysis of small drone polarimetry based on micro-Doppler signature," IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 10, 1670-1674, 2017.
doi:10.1109/LGRS.2017.2727824        Google Scholar

15., Available at http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz/.
doi:10.1109/LGRS.2017.2727824        Google Scholar

16. Chen, V. C., The Micro-Doppler Effect in Radar, Artech House, 2011.

17. Li, J., P. Wang, Y. Li, Y. Zhao, X. Liu, and K. Luan, "Transfer learning of pre-trained Inception-v3 model for colorectal cancer lymph node metastasis classification," 2018 IEEE Inter. Conf. on Mechatronics and Automation, 1650-1654, Changchun, China, Aug. 5–8, 2018.        Google Scholar

18. Du, L., L. Li, B. Wang, and J. Xiao, "Micro-doppler feature extraction based on time-frequency spectrogram for ground moving targets classification with low-resolution radar," IEEE Sensors Journal, Vol. 16, No. 10, 3756-3763, 2016.
doi:10.1109/JSEN.2016.2538790        Google Scholar

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