Inverse Scattering by Perfectly Electric Conducting (PEC) Rough Surfaces: An Equivalent Model With Line Sources

Sefer A., Yapar A.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol.60, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 60
  • Publication Date: 2022
  • Doi Number: 10.1109/tgrs.2022.3210657
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Geobase, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Keywords: Equivalent line source, inverse electromagnetic scattering, Newton method, rough surface reconstruction, RECONSTRUCTION
  • Istanbul Technical University Affiliated: Yes


This article presents a new method for the reconstruction of the perfectly electric conducting (PEC) rough surface profiles by utilizing electromagnetic waves. The inaccessible rough surface is illuminated by a tapered plane electromagnetic wave, and the scattered field data are measured on a certain number of points above the surface under test. The method for the inverse electromagnetic imaging problem is based on a special representation of the scattered field in terms of a finite number of fictitious discrete line sources located along a plane below the rough surface. The current densities of these fictitious sources are obtained through the regularized solution of an illposed problem. Then, it is shown that the image of the rough surface can be directly retrieved by seeking the points in the space where the tangential component of the total electric field vanishes. Alternatively, a much more rigorous iterative method based on a regularized Newton algorithm is also presented. A comprehensive numerical analysis is provided to demonstrate the feasibility of the presented approach. In this context, the quantitative successes of both approaches are interpreted by considering a very sensitive l(2)-norm-based error function between the actual and the reconstructed surface profiles. Regarding different scattering scenarios taken into account, the error values obtained for satisfactory reconstructions are generally in the range of 10%-30% for both methods. It is also shown that the presented algorithms are capable of reconstructing the rough surfaces, which oscillate for every lambda horizontally and have a peak-to-peak variation of 0.5 lambda at most.