volume | PIER Journals

Abstract

This paper proposes a morphological ultra-wideband (UWB)-radar-based respiratory signal model. According to the detection theory, it is crucial to set up an appropriate model to fulfil the detection purpose. Previous models pay less attention on the time dimension of the respiratory signal, but the frequency domain cannot precisely describe it because of its non-linearity and non-stationarity. This model uses a morphological operator to dilate or erode the base wavelet, and the length and value of the digit in the structure element serve as the parameters in this morphological model. The result of the experiment carried out on 10 human targets with impulse radio ultra-wideband (IR-UWB) radar proves the efficiency of this model. As the UWB radar sensed human respiratory signal is nonlinear and non-stationary, the parameters in the model can be regarded as a measure of non-linearity and non-stationarity. An experiment is carried out with the simulated respiratory signal generated with the proposed model. The result shows that the detection algorithm based on Ensemble Empirical Mode Decomposition (EEMD) method has a better performance than that based on Adaptive Line Enhancer (ALE) and with the value of the digit in the structure element increases, the performance of the ALE method declines, while the EEMD method stays in a good performance, which indicates that the EEMD method has a good potential to deal with the nonlinear and non-stationary respiratory signal.

1. Lazaro, A., D. Girbau, and R. Villarino, "Analysis of vital signs monitoring using an IR-UWB radar," Progress In Electromagnetics Research, Vol. 100, 265-284, 2010.
doi:10.2528/PIER09120302

2. Chang, S., N. Mitsumoto, and J. W. Burdick, "An algorithm for UWB radar-based human detection," IEEE Radar Conference, 2009, 1-6, IEEE, Pasadena, CA, USA, May 4–8, 2009.

3. Yarovoy, A. G., L. P. Ligthart, J. Matuzas, and B. Levitas, "UWB radar for human being detection," IEEE Aerospace and Electronic Systems Magazine, Vol. 21, 22-26, 2006.
doi:10.1109/MAES.2006.284354

4. Liu, L., Z. Liu, and B. E. Barrowes, "Through-wall bio-radiolocation with UWB impulse radar: Observation, simulation and signal extraction," IEEE J-STARS, Vol. 4, 791-798, 2011.

5. Li, Z., W. Li, H. Lv, Y. Zhang, X. Jing, and J. Wang, "A novel method for respiration-like clutter cancellation in life detection by dual-frequency IR-UWB radar," IEEE Transactions on Microwave Theory and Techniques, Vol. 61, 2086-2092, 2013.
doi:10.1109/TMTT.2013.2247054

6. Liu, M., S. Li, H. Lv, Y. Tian, G. Lu, Y. Zhang, Z. Li, W. Li, X. Jing, and J. Wang, Human Detection Algorithm Based on Bispectrum Analysis for IR-UWB Radar, 674-681, Springer Berlin Heidelberg, 2013.

7. Li, J., L. Liu, Z. Zeng, and F. Liu, "Advanced signal processing for vital sign extraction with applications in UWB radar detection of trapped victims in complex environments," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, 783-791, 2014.
doi:10.1109/JSTARS.2013.2259801

8. Mabrouk, M., S. Rajan, M. Bolic, I. Batkin, H. R. Dajani, and V. Z. Groza, "Detection of human targets behind the wall based on singular value decomposition and skewness variations," IEEE Radar Conference 2014, 1466-1470, IEEE, Cincinnati, OH, USA, May 19–23, 2014.

9. Sengupta, S. K., Fundamentals of Statistical Signal Processing: Estimation Theory, 465-466, PTR Prentice Hall, 1993.

10. Li, X., D. Qiao, and Y. Li, "An analytical model for regular respiratory signal," Conf. Proc. IEEE Eng. Med. Biol. Soc., 102-105, 2014.

11. Serra, J., Image Analysis and Mathematical Morphology, 536, Academic Press, 1982.

12. Zhang, F. and Y. Lian, "QRS detection based on multiscale mathematical morphology for wearable ECG devices in body area networks," IEEE Transactions on Biomedical Circuits and Systems, Vol. 3, 220, 2009.
doi:10.1109/TBCAS.2009.2020093

13. Xu, G., J. Wang, Q. Zhang, S. Zhang, and J. Zhu, "A spike detection method in EEG based on improved morphological filter," Computers in Biology and Medicine, Vol. 37, 1647, 2007.
doi:10.1016/j.compbiomed.2007.03.005

14. Dogdas, B., D. W. Shattuck, and R. M. Leahy, "Segmentation of skull and scalp in 3-D human MRI using mathematical morphology," HUM BRAIN MAPP, Vol. 26, 273-285, 2005.
doi:10.1002/hbm.20159

15. Nezirovic, A., A. G. Yarovoy, and L. P. Ligthart, "Experimental study on human being detection using UWB radar," IEEE International Radar Symposium 2006, IRS 2006, 1-4, 2006.

16. Karlen, W., S. Raman, J. M. Ansermino, and G. A. Dumont, "Multiparameter respiratory rate estimation from the photoplethysmogram," IEEE Trans. Biomed. Eng., Vol. 60, 1946-1953, 2013.
doi:10.1109/TBME.2013.2246160

17. Chang, C. and G. H. Glover, "Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI," NEUROIMAGE, Vol. 47, 1381-1393, 2009.
doi:10.1016/j.neuroimage.2009.04.048

18. Lv, H., W. Li, Z. Li, Y. Zhang, T. Jiao, H. Xue, M. Liu, X. Jing, and J. Wang, "Characterization and identification of IR-UWB respiratory-motion response of trapped victims," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, 7195-7204, 2014.
doi:10.1109/TGRS.2014.2309141

19. Lv, H., T. Jiao, Y. Zhang, Q. An, M. Liu, F. Liang, X. Jing, and J. Wang, "An adaptive-MSSA-based algorithm for detection of trapped victims using UWB radar," IEEE Geoscience and Remote Sensing Letters, Vol. 12, 1808-1812, 2015.
doi:10.1109/LGRS.2015.2427835

20. Reddy, V. U. and A. Nehorai, "Response of adaptive line enhancer to a sinusoid in lowpass noise," IEE Proceedings F - Communications, Radar and Signal Processing, Vol. 128, 161-166, 1981.
doi:10.1049/ip-f-1.1981.0026

21. Li, W. Z., Z. Li, H. Lv, G. Lu, Y. Zhang, X. Jing, S. Li, and J. Wang, "A new method for non-line-of-sight vital sign monitoring based on developed adaptive line enhancer using low centre frequency UWB radar," Progress In Electromagnetics Research, Vol. 133, 535-554, 2013.
doi:10.2528/PIER12093002

22. Benesty, J., J. D. Chen, and Y. T. Huang, "Time-delay estimation via linear interpolation and cross correlation," IEEE Transactions on Speech and Audio Processing, Vol. 12, 509-519, 2004.
doi:10.1109/TSA.2004.833008

23. Lei, Y., Z. He, and Y. Zi, "Application of the EEMD method to rotor fault diagnosis of rotating machinery," Mechanical Systems and Signal Processing, Vol. 23, 1327-1338, 2009.
doi:10.1016/j.ymssp.2008.11.005

24. Wu, Z. and N. E. Huang, "Ensemble empirical mode decomposition: A noise-assisted data analysis method," Advances in Adaptive Data Analysis, Vol. 1, 1-41, 2011.
doi:10.1142/S1793536909000047

Dr. Miao Liu

Dr. Hui Jun Xue

Dr. Fulai Liang

Dr. Hao Lv

Dr. Zhao Li

Dr. Fu Gui Qi

Dr. Ziqi Zhang

Prof. Jianqi Wang