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Title: An automated system for performance assessment of airport lighting
Author: Niblock, James Hagan
ISNI:       0000 0001 3445 7445
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2009
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The development of an autonomous system for the accurate measurement of the quality of aerodrome ground lighting (AGL) in accordance with current standards and recommendations is presented. The system is composed of an imager which is placed inside the cockpit of an aircraft. This is used to record images of the AGL during a normal descent to an aerodrome. Using these images, two techniques are proposed to assess the performance of the AGL. The first, termed uniformity, assesses the performance of the AGL based on the average pixel grey level (APGL) of each extracted luminaire. The second technique assesses the individual luminous intensity of each luminaire in the AGL based on its total grey level (TGL). Before the performance of the AGL is assessed, it is first necessary to uniquely identify each luminaire within the image data and track them through an image sequence. A new model-based methodology is proposed to overcome inefficfencies with existing techniques. The new approach utilises projective geometry in order to find the optimum match between a template of the AGL and the actual image data using non-linear least-squares optimisation. The algorithms return a set of uniquely identified luminaires with the associated camera position for each frame. The uniformity software uses this information to group the luminaires into bands which have similar luminous intensity in accordance with the relevant standards and a search is conducted within each band to classify the luminaires according to their APGL and TGL. The luminaires are grouped into one of four classifications: fail, under-perform, pass and over-perform; with failing luminaires automatically highlighted by the system. A new intensity model is then presented which determines the luminous intensity of each uniquely identified luminaire. Both techniques are validated on real and synthetic image data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available