[Bayesian representation of prior information and MCMC method in microwave imaging].

Microwave imaging for dielectric objects was considered in this paper. Applying Bayesian approach to represent prior information about permittivity distribution of observed object by prior probability density and combine measurements information of scattering field, we obtained posterior probability density that included synthetic information about the observed object. And then, Gibbs sampler, one of Markov Chain Monte Carlo method, was used to sample the posterior probability density. The sample mean was regarded as an evaluation of the permittivity distribution. The results of simulation imaging with "blocky" objects showed that this set of methods made good use of information and had the advantages of feasibility and very strong anti-noise ability. In addition,it is capable of describing (definite or indefinite) prior information in a convenient and controllable way, as well as capable of giving the "complete" solution, i.e., the occurrence probability of every permittivity distribution.