Modified Sparse Bayesian Learning-Based Multichannel Radar Forward Looking Imaging

Multichannel radar has the potential of forward-looking imaging, 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, traditional SBL methods suffer from the over-sparse problem for extended targets, and they always fails to achieve good performance when there are both point targets and extended targets. In this paper, by judging the types of targets to assign different weights for different targets, and then applying the weighted average to the update results of hyperparameters in SBL iterations, a modified SBL-based scheme of multichannel radar forward looking imaging is proposed, and simulation results are illustrated to verify its effectiveness.