\r\nis an advanced nondestructive testing and evaluation (NDT&E)

\r\ntechnique. This paper investigates the complex-valued signal

\r\nprocessing related to the near-field SAR imaging system, where

\r\nthe measurement data turns out to be noncircular and improper,

\r\nmeaning that the complex-valued data is correlated to its complex

\r\nconjugate. Furthermore, we discover that the degree of impropriety

\r\nof the measurement data and that of the target image can be highly

\r\ncorrelated in near-field SAR imaging. Based on these observations, A

\r\nmodified generalized sparse Bayesian learning algorithm is proposed,

\r\ntaking impropriety and noncircularity into account. Numerical results

\r\nshow that the proposed algorithm provides performance gain, with the

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