Correlated Sample-Based Prior in Bayesian Inversion Framework for Microwave Tomography

When using the statistical inversion framework in microwave tomography (MWT), generally, real and imaginary parts of unknown dielectric constant are treated as uncorrelated independent random variables. Thereby, maximum a posteriori estimates, two recovered variables may show different structural changes inside imaging domain. In this work, a correlated sample-based prior model is presented to incorporate correlation part with framework. The method used estimate inhomogeneous moisture distribution (as constant) large cross section polymer foam. targeted application MWT industrial drying derive intelligent control methods based on tomographic inputs for selective heating purposes. One features proposed shows how integrate lab-based characterization, often available cases, modeling. validated numerical experimental data considered distributions.