Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse Non-Uniform Linear Arrays

By Giacomo Oliveri, Andrea Massa. IEEE Transactions on Antennas and Propagation, 2011

284 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Reducing the Number of Elements in Multiple-Pattern Linear Arrays by the Extended Matrix Pencil Methods (2014, methodology): “For the synthesis of a nonuniformly spaced array with single-pattern, many practical methods have been proposed, such as dynamic programming [10], analytical or deterministic methods [11], [12], Bayesian compressive sampling techniques [13], [14], and stochastic or metaheuristic optimization algorit”
Unconventional Phased Array Architectures and Design Methodologies—A Review (2016, methodology): “Accordingly, the synthesis of maximally sparse linear and planar arrays has been addressed in [77] and [78] for the generation of symmetric patterns with real-valued excitations through a Bayesian CS approach (BCS).”
Maximally Sparse Arrays Via Sequential Convex Optimizations (2012, background): “In the proposed synthesis approach, differently from [5], [6], and [8]–[10], only the upper bound of the power pattern is assigned (and not a reference field, in amplitude and phase), thus allowing to determine the minimum number of required sensors independently on the particular reference field.”
Hybrid BCS-Deterministic Approach for Sparse Concentric Ring Isophoric Arrays (2015, methodology): “Existing BCS methods [17], [19]–[21] cannot directly handle the CRIA synthesis since enforcing isophoric excitations is not allowed.”