In this paper, a new approach based on the Bayesian compressive sampling (BCS ) and within the contrast source formulation of an inverse scattering problem is proposed for imaging sparse scatterers. By enforcing a probabilistic hierarchical prior as a sparsity regularization constraint, the problem is solved by means of a fast relevance vector machine. The effectiveness and robustness of the BCS-based approach are assessed through a set of numerical experiments concerned with various scatterer configurations and different noisy conditions.

A bayesian compressive sampling-based inversion for imaging sparse scatterers

Oliveri, Giacomo;Rocca, Paolo;Massa, Andrea
2011-01-01

Abstract

In this paper, a new approach based on the Bayesian compressive sampling (BCS ) and within the contrast source formulation of an inverse scattering problem is proposed for imaging sparse scatterers. By enforcing a probabilistic hierarchical prior as a sparsity regularization constraint, the problem is solved by means of a fast relevance vector machine. The effectiveness and robustness of the BCS-based approach are assessed through a set of numerical experiments concerned with various scatterer configurations and different noisy conditions.
2011
10
Oliveri, Giacomo; Rocca, Paolo; Massa, Andrea
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/89698
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