Burak Cevat Civek | Searcharxiv

arXiv2021

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

In this paper, we investigate the problem of inverse electromagnetic scattering to recover multilayer human tissue profiles using ultrawideband radar systems in a Bayesian setting. We study the recovery problem in a blind setting, in which we simultaneously estimate both the dielectric/geometric properties of the one-dimensional target tissue profile and the transmitted radar waveform. To perform Bayesian parameter estimation, we propose a hybrid and adaptive Markov Chain Monte Carlo method, which combines the Slice sampling and Hamiltonian Monte Carlo approaches. The introduced sampling mechanism also incorporates the Parallel Tempering approach to escape from the local optimal regions of the complex posterior distribution. We provide empirical support through various numerical simulations for the achieved enhanced sampling efficiency compared to conventional sampling schemes. To investigate the recovery performance, we work on synthetic measurements simulating actual radar returns from multilayer tissue profiles. We derive theoretical bounds for the best achievable estimation performance in terms of normalized root mean square error and provide a comparison with the performance of our estimator.

eess.SP↗

arXiv2021

Bayesian Sparse Blind Deconvolution Using MCMC Methods Based on Normal-Inverse-Gamma Prior

Bayesian estimation methods for sparse blind deconvolution problems conventionally employ Bernoulli-Gaussian (BG) prior for modeling sparse sequences and utilize Markov Chain Monte Carlo (MCMC) methods for the estimation of unknowns. However, the discrete nature of the BG model creates computational bottlenecks, preventing efficient exploration of the probability space even with the recently proposed enhanced sampler schemes. To address this issue, we propose an alternative MCMC method by modeling the sparse sequences using the Normal-Inverse-Gamma (NIG) prior. We derive effective Gibbs samplers for this prior and illustrate that the computational burden associated with the BG model can be eliminated by transferring the problem into a completely continuous-valued framework. In addition to sparsity, we also incorporate time and frequency domain constraints on the convolving sequences. We demonstrate the effectiveness of the proposed methods via extensive simulations and characterize computational gains relative to the existing methods that utilize BG modeling.

stat.ME↗