Array failure diagnosis using sparse excitation source reconstruction: Reliability against position of near field observations

Zhang, Y.; Zhao, Huapeng; Li, E.-P.

doi:10.1109/ieee-iws.2014.6864185

Cited by 3 publications

(1 citation statement)

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“…In particular, many of these approaches have been inspired, by the compressive sensing paradigm [7], that exploits the knowledge of the sparsity of the chosen representation of the source to reduce the number of needed measurements. Some of these approaches have been aimed to the estimation of the far-field pattern from a limited number of points [8][9][10][11][12][13][14][15][16][17]; other methods have been developed for the identification of the failures of the radiating elements of arrays [18][19][20][21][22]. A review of the NF measurement techniques is reported in [23].…”

Section: Introductionmentioning

confidence: 99%

Accurate Reconstruction of the Radiation of Sparse Sources from a Small Set of Near-Field Measurements by Means of a Smooth-Weighted Norm for Cluster-Sparsity Problems

Pinchera

Migliore

2021

Electronics

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The aim of this contribution is to present an approach that allows to improve the quality of the reconstruction of the far-field from a small number of measured samples by means of sparse recovery using a relatively coarse grid for source positions (with sample spacing of the order of λ/8) compared to the grid usually required. In particular, the iterative method proposed employs a smooth-weighted constrained minimization, that guarantees a better probability of correct estimate of the sparse sources and an improved quality in the reconstruction, with a similar computational effort respect to the standard ℓ1 re-weighted minimization approach.

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