arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.03363math.NAcs.NA
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一种针对被动逆障碍散射问题的新型直接成像方法

A novel direct imaging method for passive inverse obstacle scattering problem

Yunwen Yin, Liang Yan

AI总结:

本文提出双重互相关方法(DCM),利用随机点源被动测量实现声软障碍物直接成像,通过互相关处理入射源不可控性并结合亥姆霍兹-基尔霍夫恒等式构建成像函数,数值实验验证其高效稳定。

摘要:

本文研究了利用随机分布点源的被动测量来恢复声软障碍物的逆散射问题。这些源引入的随机性带来了显著挑战,导致依赖散射场测量的经典直接采样方法失效。为解决这一问题,我们提出了双重互相关方法(DCM),这是一种新型直接成像方案,包含两个主要步骤。首先,DCM 在两个被动测量之间建立互相关。这种专门设计的互相关有效处理了入射源的不可控性,并通过亥姆霍兹-基尔霍夫恒等式与主动散射模型建立联系。随后,利用该互相关构建基于相关性的成像函数,能够定性识别障碍物。我们从理论上分析了 DCM 的稳定性和分辨率。大量数值算例,包括两个紧密相邻障碍物和多尺度障碍物的场景,表明 DCM 计算高效、稳定且快速。

英文摘要:

This paper investigates the inverse scattering problem of recovering a sound-soft obstacle using passive measurements taken from randomly distributed point sources. The randomness introduced by these sources poses significant challenges, leading to the failure of classical direct sampling methods that rely on scattered field measurements. To address this issue, we introduce the Doubly Cross-Correlating Method (DCM), a novel direct imaging scheme that consists of two major steps. Initially, DCM creates a cross-correlation between two passive measurements. This specially designed cross-correlation effectively handles the uncontrollability of incident sources and connects to the active scattering model via the Helmholtz-Kirchhoff identity. Subsequently, this cross-correlation is used to create a correlation-based imaging function that can qualitatively identify the obstacle. The stability and resolution of DCM are theoretically analyzed. Extensive numerical examples, including scenarios with two closely positioned obstacles and multiscale obstacles, demonstrate that DCM is computationally efficient, stable, and fast.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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