Search search
Publication Date
to
Vol. 42
Latest Volume
All Volumes
PIERM 139 PIERM 138 PIERM 137 PIERM 136 PIERM 135 PIERM 134 PIERM 133 PIERM 132 PIERM 131 PIERM 130 PIERM 129 PIERM 128 PIERM 127 PIERM 126 PIERM 125 PIERM 124 PIERM 123 PIERM 122 PIERM 121 PIERM 120 PIERM 119 PIERM 118 PIERM 117 PIERM 116 PIERM 115 PIERM 114 PIERM 113 PIERM 112 PIERM 111 PIERM 110 PIERM 109 PIERM 108 PIERM 107 PIERM 106 PIERM 105 PIERM 104 PIERM 103 PIERM 102 PIERM 101 PIERM 100 PIERM 99 PIERM 98 PIERM 97 PIERM 96 PIERM 95 PIERM 94 PIERM 93 PIERM 92 PIERM 91 PIERM 90 PIERM 89 PIERM 88 PIERM 87 PIERM 86 PIERM 85 PIERM 84 PIERM 83 PIERM 82 PIERM 81 PIERM 80 PIERM 79 PIERM 78 PIERM 77 PIERM 76 PIERM 75 PIERM 74 PIERM 73 PIERM 72 PIERM 71 PIERM 70 PIERM 69 PIERM 68 PIERM 67 PIERM 66 PIERM 65 PIERM 64 PIERM 63 PIERM 62 PIERM 61 PIERM 60 PIERM 59 PIERM 58 PIERM 57 PIERM 56 PIERM 55 PIERM 54 PIERM 53 PIERM 52 PIERM 51 PIERM 50 PIERM 49 PIERM 48 PIERM 47 PIERM 46 PIERM 45 PIERM 44 PIERM 43 PIERM 42 PIERM 41 PIERM 40 PIERM 39 PIERM 38 PIERM 37 PIERM 36 PIERM 35 PIERM 34 PIERM 33 PIERM 32 PIERM 31 PIERM 30 PIERM 29 PIERM 28 PIERM 27 PIERM 26 PIERM 25 PIERM 24 PIERM 23 PIERM 22 PIERM 21 PIERM 20 PIERM 19 PIERM 18 PIERM 17 PIERM 16 PIERM 14 PIERM 13 PIERM 12 PIERM 11 PIERM 10 PIERM 9 PIERM 8 PIERM 7 PIERM 6 PIERM 5 PIERM 4 PIERM 3 PIERM 2 PIERM 1
All Journals
PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2015-06-25
Through Wall Detection with Relevance Vector Machine
By
Fang-Fang Wang,

    Dr. Fang-Fang Wang

    School of Electronics Science and Engineering

    Nanjing University of Posts and Telecommunications

    China

    Homepage

Ye-Rong Zhang,

    Dr. Ye-Rong Zhang

    Nanjing University of Posts and Telecommunications

    China

    Homepage

Hua-Mei Zhang,

    Dr. Hua-Mei Zhang

    School of Electronic Science and Engineering

    Nanjing University of Posts and Telecommunications

    China

    Homepage

Lin Hai,

    Dr. Lin Hai

    School of Electronic Science and Engineering

    Nanjing University of Posts and Telecommunications

    China

    Homepage

Gong Chen

    Dr. Gong Chen

    School of Electronic Science and Engineering

    Nanjing University of Posts and Telecommunications

    China

    Homepage

Progress In Electromagnetics Research M, Vol. 42, 169-177, 2015
doi:10.2528/PIERM15050502 | Google Scholar

Abstract
In this paper, through-wall detection problem using a data-driven model is addressed. The original problem is cast into a regression one and successively solved by means of the relevance vector machine (RVM). Multiple scattering is included in the nonlinear relationship between the feature vector extracted from the backscattered field and the position of the target obtained through a training phase using RVM; hence the nonlinearity inherent in the problem is considered. Besides, the presence of the wall is also contained in this relationship. The predictions obtained by RVM are probabilistic which capture uncertainty, and we can define error-bars for the predicted results. Therefore, the ill-posed nature of the problem is accounted for naturally, rather than using other regularization schemes. To access the effectiveness, accuracy and robustness of the proposed approach, numerical results related to a two-dimensional geometry are presented. This method is demonstrated efficient qualitatively and quantitatively.
Download Full Article (804) View Full Article volume
Citation
Fang-Fang Wang, Ye-Rong Zhang, Hua-Mei Zhang, Lin Hai, and Gong Chen, "Through Wall Detection with Relevance Vector Machine," Progress In Electromagnetics Research M, Vol. 42, 169-177, 2015.
doi:10.2528/PIERM15050502
References

1. Song, L. P., C. Yu, and Q. H. Liu, "Through-wall imaging (TWI) by radar: 2-D tomographic results and analyses," IEEE Trans. Geosci. Remot. Sens., Vol. 43, No. 12, 2793-2798, 2005.
doi:10.1109/TGRS.2005.857914        Google Scholar

2. Baranoski, E. J., "Through-wall imaging: Historical perspective and future directions," J. Frank. Inst., Vol. 345, No. 6, 556-569, 2008.
doi:10.1016/j.jfranklin.2008.01.005        Google Scholar

3. Ferris, D. D. and N. C. Currie, "Survey of current technologies for through-the-wall surveillance (TWS)," Proc. SPIE, Vol. 3577, 62-72, 1999.
doi:10.1117/12.336988        Google Scholar

4. Soldovieri, F., F. Ahmad, and R. Solimene, "Validation of microwave tomographic inverse scattering approach via through-the-wall experiments in semicontrolled conditions," IEEE Geosc. Rem. Sens. Lett., Vol. 8, No. 1, 123-127, 2011.
doi:10.1109/LGRS.2010.2051014        Google Scholar

5. Ahmad, F., M. G. Amin, and S. A. Kassam, "Synthetic aperture beamformer for imaging through a dielectric wall," IEEE Trans. Aerosp. Electron. Syst., Vol. 41, No. 1, 271-283, 2005.
doi:10.1109/TAES.2005.1413761        Google Scholar

6. Ahmad, F., M. G. Amin, and G. Mandapati, "Autofocusing of through-the-wall radar imagery under unknown wall characteristics," IEEE Trans. on Image Proc., Vol. 16, No. 7, 1785-1795, 2007.
doi:10.1109/TIP.2007.899030        Google Scholar

7. Dehmollaian, M. and K. Sarabandi, "Refocusing through building walls using synthetic aperture radar," IEEE Trans. Geosci. Remot. Sens., Vol. 46, No. 6, 1589-1599, 2008.
doi:10.1109/TGRS.2008.916212        Google Scholar

8. Soldovieri, F. and R. Solimene, "Through-wall imaging via a linear inverse scattering algorithm," IEEE Geosc. Rem. Sens. Lett., Vol. 4, No. 4, 513-517, 2007.
doi:10.1109/LGRS.2007.900735        Google Scholar

9. Soldovieri, F., R. Solimene, and G. Prisco, "A multiarray tomographic approach for through-wall imaging," IEEE Trans. Geosci. Remot. Sens., Vol. 46, No. 4, 1192-1199, 2008.
doi:10.1109/TGRS.2008.915754        Google Scholar

10. Chew, W. C. and Y. M. Wang, "Reconstruction of 2-dimensional permittivity distribution using the distorted born Iterative method," IEEE Trans. Med. Imag., Vol. 9, No. 2, 218-225, 1990.
doi:10.1109/42.56334        Google Scholar

11. Cui, T. J., W. C. Chew, A. A. Aydiner, and S. Y. Chen, "Inverse scattering of two-dimensional dielectric objects buried in a lossy earth using the distorted born iterative method," IEEE Trans. Geosci. Remot. Sens., Vol. 39, No. 2, 339-346, 2001.
doi:10.1109/36.905242        Google Scholar

12. Harada, H., D. J. N. Wall, T. Takenaka, and M. Tanaka, "Conjugate-gradient method applied to inverse scattering problem," IEEE Trans. Antennas Propagat., Vol. 43, No. 8, 784-792, 1995.
doi:10.1109/8.402197        Google Scholar

13. Kim, Y. and H. Ling, "Through-wall human tracking with multiple doppler sensors using an artificial neural network," IEEE Trans. Antennas Propagat., Vol. 57, No. 7, 2116-2122, 2009.
doi:10.1109/TAP.2009.2021871        Google Scholar

14. Kim, Y. and H. Ling, "Human activity classification based on micro-doppler signatures using a support vector machine," IEEE Trans. Geosci. Remot. Sens., Vol. 47, No. 5, 1328-1337, 2009.
doi:10.1109/TGRS.2009.2012849        Google Scholar

15. Wang, F. F. and Y. R. Zhang, "A real-time through-wall detection based on support vector machine," Journal of Electromagnetic Waves and Applications, Vol. 25, No. 1, 75-84, 2011.
doi:10.1163/156939311793898396        Google Scholar

16. Tipping, M. E., "Sparse Bayesian learning and the relevance vector machine," Journal of Machine Learning Research, Vol. 1, No. 3, 211-244, 2001.        Google Scholar

Download Full Article (804) View Full Article volume


The EM Academy piers.org jpier.org Who's Who in EM
Copyright © 2022 The Electromagnetics Academy. All Rights Reserved.