Through-the-wall imaging (TWI) of human vital signs by bioradar is a hot research topic in recent years. Unknown wall parameters (mainly thickness and dielectric constant) are huge challenges for TWI. Ambiguities in wall parameters will degrade the image focusing quality, lower signal-to-noise-clutter ratio (SNCR) of vital signs, cause vital signs to be imaged away from their true positions and blur the close vital signs from multiple humans caused by the imaging resolution declination. A through-the-wall propagation model of vital signs for multiple-input and multiple-output (MIMO) bioradar is first built to analyze the influence of wall on imaging. In order to obtain focused image of vital signs quickly, an imaging model and a novel autofocusing imaging method of vital signs are proposed in this paper. Since vital signs of human are weak and sensitive to interferences, the SNCR-enhanced imagery of vital signs after change detection (CD) is applied to evaluate the focusing quality of image. Reflections of wall in the stationary targets imaging result are line structure approximately, so Hough transform is used to extract the positions of the front edge and rear edge of wall automatically. Propagation time in the wall of electromagnetic waves is estimated and used to build the constraint relationship of wall parameters. The number of unknown parameters is reduced to only one and the efficiency of autofocusing imaging improves. Several cases, including the case of single human, multiple human objects close to each other and the case of non-human objects, are simulated. The magnetic resonance imaging (MRI) image of human chest is put into simulation scene. And then the simulation data of human vital signs are calculated by the finite-difference time-domain (FDTD) method. The results show that the proposed method can effectively estimate the wall parameters and improve the focusing performance of human vital signs. And also the kurtosis of image can be used as a feature to efficiently decide the human vital signs are existed or not. Thus the SNCR of vital signs and resolution of imaging are improved, which are beneficial for detection of vital signs. The position errors of human vital signs are also corrected.
Fu Gui Qi,
Hui Jun Xue,
the Wall Imaging of Human Vital Signs Based on UWB MIMO Bioradar," Progress In Electromagnetics Research C,
Vol. 87, 119-133, 2018. doi:10.2528/PIERC18062004
1. Li, J., L. B. Liu, Z. F. Zeng, and F. S. Liu, "Advanced signal processing for vital sign extraction with applications in UWB radar detection of trapped victims in complex environments," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., Vol. 7, 783-791, 2013.
2. Chen, K. M., D. Misra, H. E. Wang, H. R. Chuang, and E. Postow, "An X-band microwave life-detection system for searching human subjects under earthquake rubble or behind barrier," IEEE Trans. Biomed. Eng., Vol. 47, 105-114, 2000. doi:10.1109/10.817625
3. Li, Z., W. Z. Li, H. Lv, Y. Zhang, X. J. Jing, and J. Q. Wang, "A novel method for respiration-like clutter cancellation in life detection by dual-frequency IR-UWB radar," IEEE Trans. Microw. Theory Tech., Vol. 61, 2086-2092, 2013. doi:10.1109/TMTT.2013.2247054
4. Lan, F. Y., L. J. Kong, X. B. Yang, and Y. Jia, "Life-sign detection of through-wall-radar based on fourth-order cumulant," Proceedings of the Radar Conference (RADAR), Xi'an, China, Apr. 14-16, 2013.
5. Ren, L. Y., Y. S. Koo, H. F. Wang, Y. Z. Wang, Q. H. Liu, and A. E. Fathy, "Noncontact multiple heartbeats detection and subject localization using UWB impulse Doppler radar," IEEE Microwave and Wireless Components Letters, Vol. 25, No. 10, 690-692, 2015. doi:10.1109/LMWC.2015.2463214
6. Wang, F., T. Horng, K. Peng, J. Jau, J. Li, and C. Chen, "Detection ofconcealed individuals based on their vital signs by using a see-through-wall imaging system with a self-injection-locked radar," IEEE Trans. Microw. Theory Techn., Vol. 61, No. 1, 696-704, 2013. doi:10.1109/TMTT.2012.2228223
7. Liu, L. B. and S. X. Liu, "Remote detection of human vital sign with stepped-frequency continuous wave radar," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., Vol. 7, 775-782, 2014. doi:10.1109/JSTARS.2014.2306995
8. Ram, S. S. and A. Majumdar, "High-resolution radar imaging of moving humans using doppler processing and compressed sensing," IEEE Transactions on Aerospace and Electronic Systems, Vol. 51, No. 2, 1279-1287, 2015. doi:10.1109/TAES.2014.140481
9. Hu, J., Y. P. Song, T. Jin, B. Y. Lu, G. F. Zhu, and Z. M. Zhou, "Shadow effect mitigation in indication of moving human behind wall via MIMOTWRI," IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 3, 453-457, 2014.
10. Melamed, R. and N. Chayat, "Apparatus and method for doppler-assisted MIMO radar microwave imaging,", United States Patent Application, 20110237939, US, 2013.
11. Ram, S. S. and A. Majumdar, "Through-wall propagation effects on Doppler-enhanced frontal radar images of humans," IEEE Radar Conference, 1-6, 2016.
12. Wang, F. K., T. S. Horng, K. C. Peng, J. K. Jau, J. Y. Li, and C. C. Chen, "Detection of concealed individuals based on their vital signs by using a see-through-wall imaging system with a self-injection-locked radar," IEEE Trans. Microw. Theory Techn., Vol. 61, No. 1, 696-704, 2013. doi:10.1109/TMTT.2012.2228223
13. Hunt, A. R., "Use of a frequency-hopping radar for imaging and motion detection through walls," IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 5, 1402-1408, 2009. doi:10.1109/TGRS.2009.2016084
14. Liang, F. L., F. G. Qi, Q. An, H. Lv, F. M. Chen, Z. Li, and J. Q. Wang, "Detection of multiple stationary humans using UWB MIMO radar," Sensors, Vol. 16, No. 11, 2016.
15. Qu, Y., G. Liao, S.-Q. Zhu, X.-Y. Liu, and H. Jiang, "Performance analysis of beamforming for MIMO radar," Progress In Electromagnetics Research, Vol. 84, 123-134, 2008. doi:10.2528/PIER08062306
16. Zhuge, X. D. and A. G. Yarovoy, "Study on two-dimensional sparse MIMO UWB arrays for high resolution near-field imaging," IEEE Transactions on Antennas and Propagation, Vol. 60, No. 9, 4173-4182, 2012. doi:10.1109/TAP.2012.2207031
17. Muqaibel, A. H. and A. Safaai-Jazi, "A new formulation for characterization of materials based on measured insertion transfer function," IEEE Trans. Microw. Theory Tech., Vol. 51, No. 8, 1946-1951, 2003. doi:10.1109/TMTT.2003.815274
18. Muqaibel, A. H., A. Safaai-Jazi, A. Bayram, A. M. Attiya, and S. M. Riad, "Ultrawideband through-the-wall propagation," IEE Proc. Microw. Antennas Propag., Vol. 152, No. 6, 581-588, 2005. doi:10.1049/ip-map:20050092
19. Jin, T., B. Chen, and Z. M. Zhou, "Image-domain estimation of wall parameters for autofocusing of through-the-wall SAR imagery," IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 3, 1836-1843, 2013. doi:10.1109/TGRS.2012.2206395
20. Wang, G. Y. and M. G. Amin, "Imaging through unknown walls using different standoff distances," IEEE Transactions on Signal Processing, Vol. 54, No. 10, 4015-4025, 2006. doi:10.1109/TSP.2006.879325
21. Ahmad, F., M. G. Amin, and G. Mandapati, "Autofocusing of through-the-wall radar imagery under unknown wall characteristics," IEEE Trans. Image Process., Vol. 16, No. 7, 1785-1795, 2007. doi:10.1109/TIP.2007.899030
22. Al-Qadi, I. L. and S. Lahouar, "Measuring layer thicknesses with GPR theory to practice," Construction and Building Materials, Vol. 19, No. 10, 763-772, 2005. doi:10.1016/j.conbuildmat.2005.06.005
23. Aftanas, M., J. Rovnakova, M. Drutarovsky, and D. Kocur, "Efficient method of TOA estimation for through wall imaging by UWB radar," Proc. Int. Conf. Ultrawideband, 101-104, 2008.
24. Amin, M. G. and F. Ahmad, "Change detection analysis of humans moving behind walls," IEEE Transactions on Aerospace and Electronic Systems, Vol. 49, No. 3, 1410-1425, 2013. doi:10.1109/TAES.2013.6557995
25. Liang, F. L., M. Liu, H. N. Li, F. G. Qi, Z. Li, and J. Q.Wang, "Through-the-wall imagery of human vital signs using UWB MIMO bioradar," 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 924-927, Chengdu, China, Dec. 15-17, 2017.
26. Gabriel, C., "Compilation of the dielectric properties of body tissues at RF and microwave frequencies ,", Technical Report, 78235-5102, Brooks Air Force Base, Texas, 1996.