High-Resolution Imaging from Gapped Data Based on Fast Sparse Bayesian Learning

Abstract

High-resolution imaging from gapped data has become a research hotspot in radar imaging field. Among many imaging algorithms, sparse Bayesian learning (SBL) is more robust and has greater estimation accuracy, which attracts active interest from researchers. Unfortunately, the inversion and multiplying operations are involved in each iteration of SBL lead to heavy computational complexity when they are implemented directly. In this paper, we propose a fast Fourier dictionary (FD)-based SBL algorithm to solve high-resolution imaging from gapped data, greatly reducing the calculation cost. Finally, the experimental results verify the effectiveness of the proposed method.