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In this paper, we investigate the problem of inverse electromagneticscattering to recover multilayer human tissue profiles using ultrawidebandradar systems in a Bayesian setting. We study the recovery problem in a blindsetting, in which we simultaneously estimate both the dielectric/geometricproperties of the one-dimensional target tissue profile and the transmittedradar waveform. To perform Bayesian parameter estimation, we propose a hybridand adaptive Markov Chain Monte Carlo method, which combines the Slice samplingand Hamiltonian Monte Carlo approaches. The introduced sampling mechanism alsoincorporates the Parallel Tempering approach to escape from the local optimalregions of the complex posterior distribution. We provide empirical supportthrough various numerical simulations for the achieved enhanced samplingefficiency compared to conventional sampling schemes. To investigate therecovery performance, we work on synthetic measurements simulating actual radarreturns from multilayer tissue profiles. We derive theoretical bounds for thebest achievable estimation performance in terms of normalized root mean squareerror and provide a comparison with the performance of our estimator.

Blind Reconstruction of Multilayered Tissue Profiles with UWB Radar Under Bayesian Setting