A new Compressive Sensing (CS) imaging method is proposed to exploit, during the inversion process and unlike stateof- the-art CS-based approaches, additional information besides that on the target sparsity. More specifically, such an innovative multi-resolution Bayesian CS scheme profitably combines (i) the a-priori knowledge on the class of targets under investigation and (ii) the progressively-acquired information on the scatterer location and size to improve the accuracy, the robustness, and the efficiency of both standard (i.e., uniform-resolution) CS techniques and multi-resolution/synthetic-zoom approaches. Towards this end, a new multi-resolution-based information-driven relevance vector machine (RVM) is derived and implemented. Selected results from an extensive numerical and experimental validation are shown to give the interested readers some indications on the effectiveness and the reliability of the proposed method also in comparison with state-of-the-art deterministic and Bayesian inversion techniques.
Iterative Multiresolution Bayesian CS for Microwave Imaging / Anselmi, Nicola; Poli, Lorenzo; Oliveri, Giacomo; Massa, Andrea. - In: IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION. - ISSN 0018-926X. - 2018,66:7(2018), pp. 3665-3677. [10.1109/TAP.2018.2826574]
Iterative Multiresolution Bayesian CS for Microwave Imaging
Anselmi, Nicola;Poli, Lorenzo;Oliveri, Giacomo;Massa, Andrea
2018-01-01
Abstract
A new Compressive Sensing (CS) imaging method is proposed to exploit, during the inversion process and unlike stateof- the-art CS-based approaches, additional information besides that on the target sparsity. More specifically, such an innovative multi-resolution Bayesian CS scheme profitably combines (i) the a-priori knowledge on the class of targets under investigation and (ii) the progressively-acquired information on the scatterer location and size to improve the accuracy, the robustness, and the efficiency of both standard (i.e., uniform-resolution) CS techniques and multi-resolution/synthetic-zoom approaches. Towards this end, a new multi-resolution-based information-driven relevance vector machine (RVM) is derived and implemented. Selected results from an extensive numerical and experimental validation are shown to give the interested readers some indications on the effectiveness and the reliability of the proposed method also in comparison with state-of-the-art deterministic and Bayesian inversion techniques.File | Dimensione | Formato | |
---|---|---|---|
R309.pdf
Solo gestori archivio
Tipologia:
Versione editoriale (Publisher’s layout)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
3.06 MB
Formato
Adobe PDF
|
3.06 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione