Microwave Imaging of Inhomogeneous Objects Based on Bayesian Compressed Sensing

To reconstruct sparsely distributed inhomogeneous objects, a Bayesian compressed sensing microwave imaging method based on Gauss prior is proposed. In the first order Born approximation, a sparse sensing model is established based on the electric field integral equation and the mesh discretization in the imaging region. The Bayesian probability density function based on the Gauss prior is constructed. The objective function is optimized by using the relevance vector machine method. The simulation imaging of multi-target and non-uniform target is studied, and the influence of noise is considered. The results show that the reconstruction results of Bayesian compressed sensing method based on Gauss prior are better than conjugate gradient iteration algorithm and orthogonal matching pursuit compressed sensing algorithm, which verify the effectiveness and robustness of the algorithm.