TY - JOUR
T1 - On a Hybrid Approach for Recovering Multiple Obstacles
AU - Yin , Yunwen
AU - Yin , Weishi
AU - Meng , Pinchao
AU - Liu , Hongyu
JO - Communications in Computational Physics
VL - 3
SP - 869
EP - 892
PY - 2022
DA - 2022/03
SN - 31
DO - http://doi.org/10.4208/cicp.OA-2021-0124
UR - https://global-sci.org/intro/article_detail/cicp/20301.html
KW - Inverse scattering, multiple obstacles, linear sampling method, Bayesian method, hybridization.
AB - <p style="text-align: justify;">In this paper, a hybrid approach which combines linear sampling method
and the Bayesian method is proposed to simultaneously reconstruct multiple obstacles. The number of obstacles and the approximate geometric information are first
qualitatively obtained by the linear sampling method. Based on the reconstructions of
the linear sampling method, the Bayesian method is employed to obtain more refined
details of the obstacles. The well-posedness of the posterior distribution is proved by
using the Hellinger metric. The Markov Chain Monte Carlo algorithm is proposed to
explore the posterior density with the initial guesses provided by the linear sampling
method. Numerical experiments are provided to testify the effectiveness and efficiency
of the proposed method.</p>