Aiming at the difficulty of microwave imaging of strong scatterers

a multi-task Bayesian compressed sensing method based on Laplacian priori is proposed

which realizes microwave imaging of sparse strong scatterers. In the framework of contrast sources

sparse sensing model is established based on the "data" integral equation and the mesh discretization in the imaging region. The forward problem is simulated by the moment method; a Bayesian compressed sensing hierarchical model based on Laplacian priori is constructed; and in the case of multi-incident waves

multi-task Bayesian compressed sensing method is used to optimize the contrast source. Finally

the objective function is reconstructed by using the "state equation". Considering the influence of noise

Through the numerical simulation of multi-pixel single target

non-uniform single target and multi-target microwave imaging

and compared with the reconstructed results of conjugate gradient method and multi-task Bayesian compressed sensing method in the first-order Born approximation framework

which verifies the effectiveness and robustness of the proposed algorithm.