Speaker: Gao Zhiqi

Affiliation: Inner Mongolia University of Technology

Academic title: Professor

Abstract:

Compressed sensing (CS) theory is widely used in the field of radar imaging. However, synthetic aperture radar (SAR) imaging cannot effectively reconstruct the non-sparse scene by the CS theory. Hence, a novel hybrid sparse SAR imaging method based on L1/2-norm is proposed. The method uses the approximate observation operator instead of the exact observation operator for SAR imaging. It performs the sparse representation of the spatial non-sparse scene with the discrete cosine transform and the curvelet transform. Then, the optimization problem based on the L1/2-norm is solved, so as to realize the reconstruction of the non-sparse scene in SAR imaging. The imaging experiments of simulated and actual scenes demonstrated that, the proposed method can achieve the effective reconstruction of non-sparse scenes under the condition of downsampling.

Biography:

Gao Zhiqi was born in Inner Mongolia, China, in 1980. He received the B.S. and M.S. degrees in automation, both from Inner Mongolia University, China, in 2003 and 2006, respectively, and the Ph.D. degree in signal and information processing from Xidian University in 2016. His research interests include space-time adaptive processing, robust adaptive beamforming and SAR imaging.