Title :
Adaptive Sparse Sampling to Estimate Radiation and Scattering Patterns to a Specified Uncertainty with Model-Based Parameter Estimation: Compute patterns using as few as two to four samples per lobe.
Author :
Miller, Edmund K.
Author_Institution :
Los Alamos Nat. Lab., Lincoln, CA, USA
Abstract :
Analyzing antennas and scatterers in electromagnetics usually involves evaluating far-field radiation and scattering patterns. For problems involving objects, a few wavelengths to an extent, the computing time needed to evaluate a far field is usually much less than that needed to find the source distribution on that object. As the object size increases, however, the time required to obtain the far field can become significant, especially for a monostatic scattering pattern where a new source distribution occurs for each incidence angle or for the radiation and receiving patterns of large complex objects. This is especially the case if the far field is to be sampled finely enough in angle to ensure that important features of the pattern are not missed.
Keywords :
antenna radiation patterns; compressed sensing; electromagnetic wave scattering; parameter estimation; adaptive sparse sampling; antennas radiation pattern estimation; far-field radiation pattern; model-based parameter estimation; monostatic scattering pattern; source distribution; Adaptation models; Apertures; Brain modeling; Computational modeling; Electromagnetic scattering; Frequency modulation; Uncertainty;
Journal_Title :
Antennas and Propagation Magazine, IEEE
DOI :
10.1109/MAP.2015.2453920