Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.724488
Title: Improving efficiency and accuracy of safety related algorithms for unmanned aircraft systems
Author: Mishra, Chinmaya
ISNI:       0000 0004 6425 2215
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2017
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Abstract:
This thesis examines the problem of large computational loads generated by safety re- lated algorithms for Unmanned Aircraft Systems (UAS). Efficient and accurate methods for multiple sensor fault detection and Sense-And-Avoid systems for UAS are proposed. A novel sensor fault detection method is proposed and tested by simulation. The method detects multiple sensor faults by evaluating normal and faulty hypotheses for each sensor sequentially using measurements obtained from sensors on-board the air- craft. A Six-Degrees-of-Freedom flight model for a Navion aircraft is used to simulate faulty sensor data to test the fault detection method. The proposed sequential fault detection method detects faulty sensors, the update process is fast and maintains a more accurate state-estimate than the parallel fault detection method. For Sense-And-Avoid systems, an efficient method for estimating the probability of conflict between traffic in a non-cooperative environment is proposed. Estimating low probabilities of conflict using 'naive' Direct Monte Carlo method generates a significant computational load. The proposed method uses a technique called Subset Simulation where small failure probabilities are computed as a product of larger conditional failure probabilities - reducing the computational load whilst improving the accuracy of the probability estimates. The utility of the approach is demonstrated by modelling a series of conflicting and potentially conflicting scenarios based on the standard Rules of the Air specified by the International Civil Aviation Organization.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.724488  DOI: Not available
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