In this work, the synthesis problem of sparse linear arrays complying with user defined power masks is addressed by means of a Compressive Sensing (CS)-based methodology. A novel hybrid Bayesian CS (BCS) approach integrating a mask-constrained synthesis within a BCS solver is proposed, enabling an effective and efficient tool for the design of sparse arrays, starting from arbitrary user-defined requirements. A simple example validating the proposed approach is reported and discussed.
Power pattern matching through the hybrid Bayesian compressive sensing / Anselmi, N.; Oliveri, G.; Massa, A.. - STAMPA. - (2018), pp. 1-3. (Intervento presentato al convegno CAMA 2018 tenutosi a Vasteras, Sweden nel 3rd-6th September 2018) [10.1109/CAMA.2018.8530538].
Power pattern matching through the hybrid Bayesian compressive sensing
Anselmi N.;Oliveri G.;Massa A.
2018-01-01
Abstract
In this work, the synthesis problem of sparse linear arrays complying with user defined power masks is addressed by means of a Compressive Sensing (CS)-based methodology. A novel hybrid Bayesian CS (BCS) approach integrating a mask-constrained synthesis within a BCS solver is proposed, enabling an effective and efficient tool for the design of sparse arrays, starting from arbitrary user-defined requirements. A simple example validating the proposed approach is reported and discussed.File | Dimensione | Formato | |
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