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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Veröffentlicht in:	IET microwaves, antennas & propagation Jg. 13; H. 11; S. 1940 - 1947
Hauptverfasser:	Li, Wei, Deng, Weibo, Yang, Qiang, Migliore, Marco D
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 11.09.2019
Schlagworte:	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
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Zusammenfassung:	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