Sparse Bayesian 3-D Imaging for Low-RCS Objects via Dyadic Green's Function

Abstract

Sparse Bayesian (SB) 3-D imaging resolution transcends classical radar cross section (RCS) imaging, providing a novel solution for low-RCS objects. To better resolve low-RCS objects, we propose an improved SB 3-D imaging algorithm via dyadic Green's function, i.e., 1) in the building of the radar observation model, a novel dyadic Green's function-form dictionary (DGFD) is designed to replace traditional scalar form, contributing to resolving low-RCS objects; 2) in the implementation of 3-D imaging, an improved SB algorithm using DGFD is proposed, further ensuring high precision. Electromagnetic simulation results show the precision of the proposed algorithm is less than 1 dB in mean absolute deviation and less than 1.5 dB in standard deviation, better than existing algorithms.