Compressive Sensing for Monostatic Scattering From 3-D NURBS Geometries

Compressive sensing (CS) proposes to capture compressible signals at a rate significantly smaller than the Nyquist-Shannon rate, yet allows accurate signal reconstruction. CS exploits the fact that the signal of interest is compressible by a known transform (e.g., Fourier, Wavelet, etc.) and it empl...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on antennas and propagation 2016-08, Vol.64 (8), p.3545-3553
Main Authors: Chai, Shui-Rong, Guo, Li-Xin
Format: Article
Language:English
Subjects:
Algorithms
Basis functions
Compressibility
Compressive sensing (CS)
Detection
Electromagnetic scattering
Method of moments
method of moments (MoM)
monostatic scattering
Nonuniform
nonuniform rational B-splines (NURBS)
Preserves
Reconstruction
Scattering
Sensors
Splines (mathematics)
Surface reconstruction
Surface topography
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Compressive sensing (CS) proposes to capture compressible signals at a rate significantly smaller than the Nyquist-Shannon rate, yet allows accurate signal reconstruction. CS exploits the fact that the signal of interest is compressible by a known transform (e.g., Fourier, Wavelet, etc.) and it employs nonadaptive linear projections that preserve the structure of the signal. Approximate reconstruction is then obtained from these measurements by solving an optimization problem. In this paper, a new method that introduces ideas from CS to the method of moments (MoM) is proposed to solve electromagnetic monostatic scattering from conducting bodies of arbitrary shape efficiently modeled by nonuniform rational B-spline (NURBS) surfaces. The new method is applied to obtain monostatic induced currents and radar cross section values of several objects modeled with NURBS surfaces. The efficiency and accuracy of the new method are verified by comparing it against the multilevel fast multipole algorithm and the traditional MoM. The influences of the structure of the sparse basis functions and the number of measurements on the recovery error are also investigated.
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2016.2580166