The Compressive Sensing (CS) paradigm recently emerged as an effective strategy to efficiently address inverse scattering problems. Even if the sensing problem has been already widely investigated in the literature, the sampling problem (i.e. the optimization of the observation setup) is still not well addressed. In this work a new CS paradigm, namely Compressive Processing (CP), is presented, aimed to jointly solve the sampling and sensing problems for a new generation of CS- based inverse scattering techniques.
Compressive Processing in Inverse Problems: Current Advances and Future Trends / Anselmi, Nicola; Poli, Lorenzo; Oliveri, Giacomo; Massa, Andrea. - STAMPA. - (2018), pp. 219-220. (Intervento presentato al convegno 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting tenutosi a Boston, MA nel 8th-13th July 2018) [10.1109/APUSNCURSINRSM.2018.8608612].
Compressive Processing in Inverse Problems: Current Advances and Future Trends
Anselmi, Nicola;Poli, Lorenzo;Oliveri, Giacomo;Massa, Andrea
2018-01-01
Abstract
The Compressive Sensing (CS) paradigm recently emerged as an effective strategy to efficiently address inverse scattering problems. Even if the sensing problem has been already widely investigated in the literature, the sampling problem (i.e. the optimization of the observation setup) is still not well addressed. In this work a new CS paradigm, namely Compressive Processing (CP), is presented, aimed to jointly solve the sampling and sensing problems for a new generation of CS- based inverse scattering techniques.| File | Dimensione | Formato | |
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