Inverse Obstacle Scattering from Multi-Frequency Near-Field Backscattering Data
For researchers in inverse scattering, this work provides a theoretical uniqueness result and a practical algorithm for simultaneous reconstruction of shape and impedance, though the convexity assumption limits generality.
This paper tackles the inverse obstacle scattering problem of reconstructing both the geometry and boundary conditions from multi-frequency near-field backscattering data. It establishes a global uniqueness theorem and develops a three-stage numerical reconstruction framework that avoids solving the direct problem, with numerical experiments demonstrating robustness and efficiency.
This paper addresses the inverse obstacle scattering problem of simultaneously reconstructing the obstacle geometry and boundary conditions from multi-frequency near-field backscattering data. We first establish rigorous high-frequency asymptotic expansions for the scattered near-field, leveraging pseudo-differential operators (PDOs) to characterize the interaction between wavefront propagation and obstacle boundaries, where the principal symbol of the PDO governs the leading-order behavior of the scattering field. Based on these asymptotic results, we prove a global uniqueness theorem for the simultaneous recovery of the obstacle shape and impedance boundary condition under convexity assumptions. Furthermore, we develop a three-stage numerical reconstruction framework: (1) qualitative shape reconstruction via the direct sampling method; (2) quantitative boundary refinement via shape optimization; and (3) decoupled reconstruction of the boundary condition. A highlight of this algorithm is that all the three steps avoid computing the direct problem. Numerical experiments are presented to verify the robustness and efficiency of the proposed algorithm.