Multi‐Resolution Imaging through a Novel Bayesian Compressive Sensing Approach

Anselmi, N. and Poli, L. and Oliveri, G. and Massa, A. (2018) Multi‐Resolution Imaging through a Novel Bayesian Compressive Sensing Approach. Technical Report. ELEDIA Research Center - University of Trento.

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Abstract

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.

Item Type: Monograph (Technical Report)
Uncontrolled Keywords: Born approximation (BA), compressive sensing (CS), inverse scattering (IS), microwave imaging, relevance vector machine (RVM)
Subjects: A Areas > A WC Next Generation Wireless Communications
M Methodologies > M AT Analytic Techniques
M Methodologies > M CS Compressive Sensing
URI: http://www.eledia.org/students-reports/id/eprint/780

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