Sparsening Conformal Arrays Through a Versatile $BCS$-Based Method

Abstract

Sparsening conformal arrangements is carried out through a versatile Multi-Task Bayesian Compressive Sensing strategy. The problem, formulated in a probabilistic fashion as a pattern-matching synthesis, is that of determining the sparsest excitation set (locations and weights) fitting a reference pattern subject to user-defined geometrical constraints. Results from a set of representative numerical experiments are presented to illustrate the key-features of the proposed approach as well as to assess, also through comparisons, its potentials in terms of matching accuracy, element saving, and computational costs.

Compressive Sensing Multiplicative Antenna Array

“…It is noteworthy that although these approaches show effectiveness in reproducing the desired/reference patterns, some do not work for asymmetrical beams. Some recent advance methods like Bayesian compressive sensing has overcome this problem too [30]. Achieving a steerable thinned and sparse array solution requires a fully connected array hardware with RF switches where the beam projection is controlled by phase shifters or periodic time sequencing.…”

Direction of arrival estimation of sparse rectangular array via two-dimensional continuous compressive sensing

“…reducing the number of sensors, can be an effective solution which has recently been researched. There are many methods to design sparsen linear arrays (SLA) [5]- [7] and sparse rectangular arrays [8], [9] for desired beam patterns. In [10], the DOA estimation method for sparse linear arrays has been presented.…”

Hybrid BCS-Deterministic Approach for Sparse Concentric Ring Isophoric Arrays

“…Towards this end, the approach combines the Bayesian compressive sensing (BCS) technique [17] and a deterministic strategy inspired by [7] to design a CRIA with a user-defined number of elements that accurately matches a target pattern radiated by a reference continuous aperture distribution 1 . More specifically, an auxiliary concentric current ring array (CCRA) matching the reference pattern with the minimum number of rings is synthesized by means of a suitable implementation of the BCS technique [17] benefiting of its excellent performances in terms of computational efficiency (i.e., thousands of unknowns determined in few seconds) [17] and enhanced robustness compared to other CS strategies [17] as shown in similar electromagnetic problems [17]- [21]. Finally, the number and the locations of the array elements on each ring of the 1 As an example, the reference continuous aperture distribution can be determined analogously to [7], or through other convex programming strategies (see [7] and references therein).…”

“…The original methodological contributions of the communication comprise (i) the formulation of the CCRA synthesis as a CS problem, (ii) the exploitation of the BCS technique to compute the continuous current ring layout rather than the array element positions and excitations as in previous CS-based array syntheses [17], [19], [20], (iii) the introduction of a BCS-based strategy able to derive isophoric array layouts, otherwise impossible with any existing BCS-based method [17], [19]- [21], with a user-defined degree-of-sparsity, (iv) a CRIA-discretization method avoiding the innermost ring is fully populated, unlike [7].…”