In this paper, a sparsity-driven approach for the detection and characterization of small buried objects has been proposed. In the framework of the well-known Born Approximation, the theory of Compressive Sensing can help in improving reconstruction capabilities by reducing the number of data to be processed or gaining in resolution. The performance of the imaging algorithm also depends on the kind of employed configuration (single-view or multi-view), which has been explored in a preliminary numerical analysis in a simplified 2D geometry.
|Titolo:||Numerical analysis of a Compressive Sensing approach for ground penetrating radar applications|
|Autori interni:||AMBROSANIO, MICHELE|
|Data di pubblicazione:||2015|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|