Relevance vector machine technique for the inverse scattering problem

doi:10.1088/1674-1056/21/5/050204

Abstract A novel method based on the relevance vector machine (RVM) for the inverse scattering problem is presented in this paper. The nonlinearity and the ill-posedness inherent in this problem are simultaneously considered. The nonlinearity is embodied in the relation between the scattered field and the target property, which can be obtained through the RVM training process. Besides, rather than utilizing regularization, the ill-posed nature of the inversion is naturally accounted for because the RVM can produce a probabilistic output. Simulation results reveal that the proposed RVM-based approach can provide comparative performances in terms of accuracy, convergence, robustness, generalization, and improved performance in terms of sparse property in comparison with the support vector machine (SVM) based approach.

Keywords: inverse scattering problem through-wall problem relevance vector machine finite-difference time-domain

Received: 26 August 2011
Revised: 27 April 2012
Accepted manuscript online:

PACS:	02.30.Zz	(Inverse problems)
	29.40.Gx	(Tracking and position-sensitive detectors)
	41.20.Jb	(Electromagnetic wave propagation; radiowave propagation)
	81.70.Ex	(Nondestructive testing: electromagnetic testing, eddy-current testing)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61071022) and the Graduate Student Research and Innovation Program of Jiangsu Province, China (Grant No. CXZZ11-0381).

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Wang Fang-Fang(王芳芳) and Zhang Ye-Rong(张业荣) Relevance vector machine technique for the inverse scattering problem 2012 Chin. Phys. B 21 050204

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Relevance vector machine technique for the inverse scattering problem

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