Bayesian deconvolution method is an very important technique to obtain high angular resolution in forward-looking area. In the traditional Bayesian sparse representation model, each element of the target is independent of each other, but in the actual signal processing, the elements of the target are not completely independent of each other, or we can say it has block-sparse structure. Theoretically, making full use of these structural information will further improve the imaging effect. The Pattern-Coupled Sparse Bayesian Learning (PCSBL) method is an effective way to extract this kind of structural information without any prior structure information. In this paper, we try to use this method to extract the structure information of the target in the forward-looking area, and numerical experimental results show that this method is very effective for the imaging of area targets and exhibits robust properties.