TY  - RPRT
M1  - technical_report
N2  - In this work, a multi-resolution strategy is proposed for improving the reconstruction capabilities  of standard Bayesian compressive sensing (BCS) when dealing with the imaging of sparse targets. Towards this end, a customized relevance vector machine (RVM) solver is derived and implemented in order to exploit the progressively acquired information about the scatterer shape and location within the imaged domain.
Some numerical results are shown to validate the effectiveness of the proposed imaging technique.
TI  - Multi?Resolution Imaging through a Novel Bayesian Compressive Sensing Approach
Y1  - 2018///
UR  - http://www.eledia.org/students-reports/780/
AV  - public
PB  - ELEDIA Research Center - University of Trento
KW  - Born approximation (BA)
KW  -  compressive sensing (CS)
KW  -  inverse scattering (IS)
KW  -  microwave imaging
KW  -  relevance vector machine (RVM)
A1  - Anselmi, N.
A1  - Poli, L.
A1  - Oliveri, G.
A1  - Massa, A.
ID  - elediasc12780
ER  -