Radar correlated imaging for extended target by the clustered sparse Bayesian learning with Laplace prior

摘要

Radar correlated imaging (RCI) is a novel modality to obtain high resolution target images by correlated process of stochastic radiation field and the received signals. Conventional RCI methods neglect the inherent structure information of complex extended target, which makes the quality of recovery result degraded. Thus a clustered sparse Bayesian learning with Laplace prior (La-CSBL) algorithm for extended target imaging is proposed in this paper. A hierarchical correlated Laplace prior model is introduced to consider both the sparse prior and the cluster prior, and the prior for each coefficient not only involves its own hyperparameter, but also its immediate neighbor hyperparameters. Then the algorithm alternates between steps of target reconstruction and parameter optimization by cyclic minimization method under the Bayesian maximum a posteriori framework. Experimental results show that the proposed algorithm could realize high resolution imaging efficiently for extended target.