%0 Journal Article
	%A Pan Long and  Bi Dongjie and  Li Xifeng and  Xie Yongle
	%D 2019
	%J International Journal of Electronics and Communication Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 149, 2019
	%T A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity
	%U https://publications.waset.org/pdf/10010396
	%V 149
	%X The near-field synthetic aperture radar (SAR) imaging
is an advanced nondestructive testing and evaluation (NDT&E)
technique. This paper investigates the complex-valued signal
processing related to the near-field SAR imaging system, where
the measurement data turns out to be noncircular and improper,
meaning that the complex-valued data is correlated to its complex
conjugate. Furthermore, we discover that the degree of impropriety
of the measurement data and that of the target image can be highly
correlated in near-field SAR imaging. Based on these observations, A
modified generalized sparse Bayesian learning algorithm is proposed,
taking impropriety and noncircularity into account. Numerical results
show that the proposed algorithm provides performance gain, with the
help of noncircular assumption on the signals.
	%P 323 - 330