An electromagnetic inverse scattering approach based on support vector machine

Wang Fang-Fang; Zhang Ye-Rong

doi:10.7498/aps.61.084101

Abstract

In order to solve electromagnetic inverse scattering problem, a real-time inverse scattering method is proposed. This technique converts the inverse scattering problem into a regressed one using support vector machine (SVM). Electromagnetic inverse scattering method based on the SVM deals with nonlinearity and ill-posedness inherent in the inverse scattering problem successfully. The feasibility and the validity are tested by making use of simulating through-wall problem, and the results demonstrate that this approach can detect and position the targets behind the wall, no matter whether there exists noise or not. In the through-wall scenario, the influence of the number of sampling positions of receiving antenna on the predicted results is discussed using the predicted model of SVM. In the end, the predicted errors are analyzed and investigated in the multiple source scenario. The results show that this kind of setting is helpful for target identification in through-wall problem.

Keywords:

Authors and contacts

1.
School of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61071022) and the Scientific Research and Innovation Program for the Graduate Students in Institution of Higher Education of Jiangsu Province, China (Grant No. CXZZ11-0381).

References

[1]	Zhang Y R, Nie Z P, Ruan Y Z 1996 J. Univ. Electron. Sci. Techn. China 25 589 (in Chinese) [张业荣, 聂在平, 阮颖铮 1996 电子科技大学学报 25 589]
[2]	Liu G D, Zhang Y R 2010 Acta Phys. Sin. 59 6969 (in Chinese) [刘广东, 张业荣 2010 59 6969]
[3]	Zhang Q H, Xiao B X, Zhu G Q 2006 Acta Geophys. Sin. 49 1546 (in Chinese) [张清河, 肖柏勋, 朱国强 2006 地球 49 1546]
[4]	Soldovieri F, Solimene R 2007 IEEE Geosci. Remote Sens. Lett. 4 513
[5]	Chew W C, Wang Y M 1990 IEEE Trans. Med. Imag. 9 218
[6]	Cui T J, Chew W C, Aydiner A A, Chen S Y 2001 IEEE Trans. Geosci. Remote Sens. 39 339
[7]	Rekanos I T, Yioultsis T V, Tsiboukis T D 1999 IEEE Trans. Microwave Theory Techn. 47 336
[8]	Song L P, Yu C, Liu Q H 2005 IEEE Trans. Geosci. Remote Sens. 43 2793
[9]	Ahmad F, Amin M G, Kassam S A 2005 IEEE Trans. Aerosp. Electron. Syst. 41 271
[10]	Kim Y, Ling H 2009 IEEE Trans. Antennas Propag. 57 2116
[11]	Sukens J A K 2000 Neural Network World 10 29
[12]	Cui W Z, Zhu C C, Bao W X, Liu J H 2004 Acta Phys. Sin. 53 3303 (in Chinese) [崔万照, 朱长纯, 保文星, 刘君华 2004 53 3303]
[13]	Vapnik V N 1995 The Nature of Statistical Learning Theory (New York: Springer-Verlag )
[14]	Wang F F, Zhang Y R 2011 J. Electromagn. Waves Appl. 25 75
[15]	Jiang B, Wang H Q, Li X, Guo G R 2006 Acta Phys. Sin. 55 3985 (in Chinese) [姜斌, 王宏强, 黎湘, 郭桂蓉 2006 55 3985]
[16]	Wu S Y, Wang Y J, Li L, Hu J S, Feng G Z, Yan H J 2007 Prog. Geophys. 22 1611 (in Chinese ) [邬世英, 王延江, 李莉, 胡加山, 冯国志, 阎汉杰 2007 地球物理学进展 22 1611]
[17]	Zhang X, Liu X M, Xiao X L, Hu W B 2005 Chin. J. Eng. Geophys. 2 191 (in Chinese) [张翔, 刘晓敏, 肖小玲, 胡文宝 2005 工程地球 2 191]
[18]	Wang F F, Zhang Y R 2011 Prog. Electromagn. Res. Lett. 23 119

Cited By

[1]	Zhang Y R, Nie Z P, Ruan Y Z 1996 J. Univ. Electron. Sci. Techn. China 25 589 (in Chinese) [张业荣, 聂在平, 阮颖铮 1996 电子科技大学学报 25 589]
[2]	Liu G D, Zhang Y R 2010 Acta Phys. Sin. 59 6969 (in Chinese) [刘广东, 张业荣 2010 59 6969]
[3]	Zhang Q H, Xiao B X, Zhu G Q 2006 Acta Geophys. Sin. 49 1546 (in Chinese) [张清河, 肖柏勋, 朱国强 2006 地球 49 1546]
[4]	Soldovieri F, Solimene R 2007 IEEE Geosci. Remote Sens. Lett. 4 513
[5]	Chew W C, Wang Y M 1990 IEEE Trans. Med. Imag. 9 218
[6]	Cui T J, Chew W C, Aydiner A A, Chen S Y 2001 IEEE Trans. Geosci. Remote Sens. 39 339
[7]	Rekanos I T, Yioultsis T V, Tsiboukis T D 1999 IEEE Trans. Microwave Theory Techn. 47 336
[8]	Song L P, Yu C, Liu Q H 2005 IEEE Trans. Geosci. Remote Sens. 43 2793
[9]	Ahmad F, Amin M G, Kassam S A 2005 IEEE Trans. Aerosp. Electron. Syst. 41 271
[10]	Kim Y, Ling H 2009 IEEE Trans. Antennas Propag. 57 2116
[11]	Sukens J A K 2000 Neural Network World 10 29
[12]	Cui W Z, Zhu C C, Bao W X, Liu J H 2004 Acta Phys. Sin. 53 3303 (in Chinese) [崔万照, 朱长纯, 保文星, 刘君华 2004 53 3303]
[13]	Vapnik V N 1995 The Nature of Statistical Learning Theory (New York: Springer-Verlag )
[14]	Wang F F, Zhang Y R 2011 J. Electromagn. Waves Appl. 25 75
[15]	Jiang B, Wang H Q, Li X, Guo G R 2006 Acta Phys. Sin. 55 3985 (in Chinese) [姜斌, 王宏强, 黎湘, 郭桂蓉 2006 55 3985]
[16]	Wu S Y, Wang Y J, Li L, Hu J S, Feng G Z, Yan H J 2007 Prog. Geophys. 22 1611 (in Chinese ) [邬世英, 王延江, 李莉, 胡加山, 冯国志, 阎汉杰 2007 地球物理学进展 22 1611]
[17]	Zhang X, Liu X M, Xiao X L, Hu W B 2005 Chin. J. Eng. Geophys. 2 191 (in Chinese) [张翔, 刘晓敏, 肖小玲, 胡文宝 2005 工程地球 2 191]
[18]	Wang F F, Zhang Y R 2011 Prog. Electromagn. Res. Lett. 23 119

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Catalog

Vol. 61, Issue 8 - 2012-04-20

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