揭示基尔霍夫偏移在实验菲涅尔数据集下有效目标检测中的新见解
Unveiling novel insights into Kirchhoff migration for effective object detection using experimental Fresnel dataset
AI总结：
本研究利用实验菲涅尔数据集，通过贝塞尔函数级数公式化基尔霍夫偏移的成像函数，论证其在有限孔径逆散射中快速识别未知物体的适用性与唯一确定性。
摘要：
本研究探讨了基尔霍夫偏移（KM）在真实世界有限孔径逆散射问题中快速识别未知物体的适用性。为论证其适用性的理论基础（包括物体的唯一确定性），基于散射场的积分方程公式，利用第一类整数阶贝塞尔函数的均匀收敛无穷级数对KM的成像函数进行了公式化。利用实验菲涅尔数据集进行的数值模拟被展示，以实现理论结果。
英文摘要：
This study investigates the applicability of Kirchhoff migration (KM) for a fast identification of unknown objects in a real-world limited-aperture inverse scattering problem. To demonstrate the theoretical basis for the applicability including unique determination of objects, the imaging function of the KM was formulated using a uniformly convergent infinite series of Bessel functions of integer order of the first kind based on the integral equation formula for the scattered field. Numerical simulations performed using the experimental Fresnel dataset are exhibited to achieve the theoretical results.
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。
