A CS-based strategy for the design of shaped-beam sparse arrays

Abstract

The problem of synthesizing maximally-sparse linear arrays with complex excitations is solved through a numerically-efficient approach based on the Bayesian Compressive Sampling (BCS). The array design problem is re-cast in a probabilistic framework, and a fast relevance vector machine (RVM) is employed for the computation of the optimal layout and… (More)

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