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Computational Electromagnetics for Radar Signature Reduction of Aerial Vehicles

Lim, Kheng-Hwee
http://rave.ohiolink.edu/etdc/view?acc_num=osu15935767432264

Abstract Details

2020, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
This dissertation presents a computational approach that aims to reduce the radar signature of aerial vehicles through application of radar absorbing materials (RAM). The process is carried out iteratively using a seek-and-treat strategy to suppress the electromagnetic backscattering over a given observable sector. To identify the locations for RAM treatment, we utilize the currents obtained from the discretized surface integral equations (SIEs) to generate the radiated-power distribution on the airframe that reveals the scattering centers. A threshold is subsequently introduced to control the area assigned for the treatment. The radiated-power distribution can also be projected into any two-dimensional plane to form a high-resolution, artifact-free radar-images for further analysis. In light with the difficulty posed by the generation of conformal discretization for a large multiscale object, we extend the SIE to handle non-conformal meshes such that the sporadic modifications of the airframe for RAM treatment can be carried out in isolation. The so-called discontinuous Galerkin surface integral equations (IEDG) allows the implementation of the combined field integral equation (CFIE) using square-integrable trial and test functions without any continuity requirements across element boundaries. In bid to enhance computation efficiency, the matrix equation derived from the IEDG is solved iteratively, wherein the required matrix-vector multiplications are accelerated using the multilevel fast multipole algorithm (MLFMA). Convergence issues that arise from presence of the air-intakes or otherwise are addressed using a scalable block-diagonal preconditioner we developed specifically for the MLFMA. It uses the Schur complementation to combine the block-inverse along the diagonal to construct larger ones in a successive stages. However, this so-called approximate direct inverse (ADI) preconditioner is not formed explicitly; or rather, it exists as an operator that only carries out the matrix-vector multiplication. Also, we exploit the low-rank property of the off-diagonal blocks to compress the key constituent in the Schur complement using a randomized scheme, wherein the required matrix-matrix multiplications between submatrices are once again accelerated by the MLFMA. Finally, the efficacy of the proposed methods are demonstrated through its application on a large-scale mockup of an aircraft.
Jin-Fa Lee (Advisor)
Robert Lee (Committee Member)
Kubilay Sertel (Committee Member)
106 p.
Electromagnetics
IEDG; ADI; RCSR; radar images

Recommended Citations

Citations

  • Lim, K.-H. (2020). Computational Electromagnetics for Radar Signature Reduction of Aerial Vehicles [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu15935767432264

    APA Style (7th edition)

  • Lim, Kheng-Hwee. Computational Electromagnetics for Radar Signature Reduction of Aerial Vehicles. 2020. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu15935767432264.

    MLA Style (8th edition)

  • Lim, Kheng-Hwee. "Computational Electromagnetics for Radar Signature Reduction of Aerial Vehicles." Doctoral dissertation, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu15935767432264

    Chicago Manual of Style (17th edition)

osu15935767432264
396
© 2020, some rights reserved.Creative Commons License Computational Electromagnetics for Radar Signature Reduction of Aerial Vehicles by Kheng-Hwee Lim is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.