Fast non-convex compressed sensing approach for diagnosis of defective array elements using planar near-field measurements

The array diagnosis method using random perturbation-convex local minimiser has to make a compromise between the probability of correct reconstruction and the computational burden. In order to overcome this limitation, in this study, a non-convex $\ell _p$ℓp ($0 \lt p \lt 1$0<p<1) norm minimis...

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Bibliographic Details
Published in:IET microwaves, antennas & propagation Vol. 13; no. 11; pp. 1940 - 1947
Main Authors: Li, Wei, Deng, Weibo, Yang, Qiang, Migliore, Marco D
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 11.09.2019
Subjects:
array diagnosis method
compressed sensing
defective array elements
differential array
failure detection
fault diagnosis
impaired planar array elements
iterative methods
least mean squares methods
mean square error
minimisation
near‐field data
nonconvex compressed sensing
nonconvex norm minimisation
perturbation technique
perturbation techniques
planar antenna arrays
planar near‐field measurements
probability
random perturbation‐convex local minimiser
random under‐sampling
Research Article
signal reconstruction
sparse excitations
nonconvex compressed sensing
iterative methods
perturbation techniques
least mean squares methods
fault diagnosis
array diagnosis method
compressed sensing
probability
random perturbation-convex local minimiser
defective array elements
planar near-field measurements
near-field data
mean square error
perturbation technique
planar antenna arrays
differential array
nonconvex norm minimisation
random under-sampling
impaired planar array elements
signal reconstruction
sparse excitations
minimisation
failure detection
ISSN:1751-8725, 1751-8733
Online Access:Get full text
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Summary:The array diagnosis method using random perturbation-convex local minimiser has to make a compromise between the probability of correct reconstruction and the computational burden. In order to overcome this limitation, in this study, a non-convex $\ell _p$ℓp ($0 \lt p \lt 1$0<p<1) norm minimisation utilising iteratively reweighted least squares algorithm for identification of impaired planar array elements is investigated. Taken into account that the number of failed elements is far less than the total array elements, the differential array composed of the healthy array and damaged array is constructed. Then the near-field data are acquired by the probe using a random under-sampling strategy. Finally, the sparse excitations of this array are estimated through the proposed algorithm and the goal of failure detection is achieved. Numerical simulation results indicate that the proposed approach lowers the mean square error of retrieved excitations compared to the non-convex approach using perturbation technique, with the advantage of a significant reduction of running time. In addition, it also improves the probability of success rate of diagnosis effectively compared to the $\ell _1$ℓ1 norm and reweighted $\ell _1$ℓ1 norm regularised methods.
ISSN:1751-8725
1751-8733
DOI:10.1049/iet-map.2019.0008