Modified SBL-based Multichannel Radar Forward-Looking Superresolution Imaging of Block-Sparse Targets

Multichannel radar has the potential of forward-looking imaging with single snapshot data, but its azimuth resolution is usually poor due to the restriction of the platform size. Many superresolution methods have been developed to improve its azimuth resolution and sparse Bayesian learning (SBL)-based methods are popular within them. However, the traditional SBL methods usually assume point sparsity of targets, which often disrupts the continuity of the target. In this paper, a forward-looking superresolution imaging scheme of block-sparse targets for multichannel radar based on modified SBL is proposed. In the scheme, the coarse estimation of target scattering coefficients is performed firstly within the framework of SBL, with which the scales of targets can be roughly determined, and the different weights are assigned correspondingly. Then the weighted average is applied during hyperparameter update in SBL to make it coupled with its neighbors and then to get the fine estimation of targets. At last, the simulated and measured data processing results are given to illustrate the effectiveness of the proposed scheme.