Detection of Defective Sensors in Phased Array Using Compressed Sensing and Hybrid Genetic Algorithm

A compressed sensing based array diagnosis technique has been presented. This technique starts from collecting the measurements of the far-field pattern. The system linking the difference between the field measured using the healthy reference array and the field radiated by the array under test is s...

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Published in:Journal of sensors 2016-01, Vol.2016 (2016), p.1-8
Main Authors: Shoaib, B., Naveed, Aqdas, Qureshi, Ijaz Mansoor, Khan, Shafqat Ullah, Basit, Abdul
Format: Article
Language:English
Subjects:
Algorithms
Arrays
Compressed
Computer simulation
Diagnosis
Genetic algorithms
Mean square errors
PCD
Satellite communications
Sensors
Signal processing
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description A compressed sensing based array diagnosis technique has been presented. This technique starts from collecting the measurements of the far-field pattern. The system linking the difference between the field measured using the healthy reference array and the field radiated by the array under test is solved using a genetic algorithm (GA), parallel coordinate descent (PCD) algorithm, and then a hybridized GA with PCD algorithm. These algorithms are applied for fully and partially defective antenna arrays. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of element failure with a small number of measurements. In the proposed algorithm, the slow and early convergence of GA has been avoided by combining it with PCD algorithm. It has been shown that the hybrid GA-PCD algorithm provides an accurate diagnosis of fully and partially defective sensors as compared to GA or PCD alone. Different simulations have been provided to validate the performance of the designed algorithms in diversified scenarios.
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subjects Algorithms
Arrays
Compressed
Computer simulation
Diagnosis
Genetic algorithms
Mean square errors
PCD
Satellite communications
Sensors
Signal processing
title Detection of Defective Sensors in Phased Array Using Compressed Sensing and Hybrid Genetic Algorithm
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