TY  - RPRT
N2  - In this work, a novel Bayesian compressive sensing (BCS)-based microwave imaging method is proposed. The developed technique suitably combines the regularization properties of CS techniques with those of the iterative multi-scale approach (IMSA), in order to exploit the progressively acquired information on the scatterer location and size and improve the overall accuracy of the retrieved images. Toward this end, an innovative information-driven relevance vector machine (RVM) has been developed. Some preliminary results are shown to verify the effectiveness of the proposed IMSA-BCS strategy.
M1  - technical_report
Y1  - 2018///
UR  - http://www.eledia.org/students-reports/777/
TI  - A Multi?Resolution Approach for BCS?Based Imaging of Sparse Scatterers
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)
AV  - public
A1  - Anselmi, N.
A1  - Poli, L.
A1  - Oliveri, G.
A1  - Massa, A.
ID  - elediasc12777
ER  -