Unscented Kalman Filtering for Real-Time Atmospheric Thermal Tracking

dc.contributor.advisorDr. Charles E. Hall, Committee Chairen_US
dc.contributor.advisorDr. Ashok Gopalarathnam, Committee Memberen_US
dc.contributor.advisorDr. Fen Wu, Committee Memberen_US
dc.contributor.authorHazard, Matthew Wesleyen_US
dc.degree.disciplineAerospace Engineeringen_US
dc.description.abstractThe increasing use of unmanned air vehicles in military and civilian applications has been accompanied by a growing demand for improved endurance and range. These demands have been largely met by advances in aerodynamic and structural efficiency, improved battery technology, and the ongoing miniaturization of onboard computing and payload systems. Recently, more attention has been paid to the extraction of energy from the atmosphere. Aircraft can make use of atmospheric updrafts, or thermals, to gain altitude without expenditure of onboard fuel stores. By intelligently tracking thermals, an unmanned aircraft can extend its range or loiter time without carrying additional fuel or specialized sensors. Prior research has focused on the `big picture' concepts associated with autonomous soaring - determining when to stop and soar in a thermal, what speed to fly, when to return to the desired course, and so on. Finding and tracking thermals is only a single component of the complete soaring system. However, because the high-level decision making tasks rely on estimates of the thermal parameters, the accuracy and computational cost of the thermal tracking algorithm set the upper performance limit of the entire system. So, this research reformulated batch regression thermal finding algorithms used in past efforts into an efficient Unscented Kalman Filter. Open-loop simulation results showed the filter was capable of accurately estimating thermal position, strength, and size with low computational cost for a variety of realistic flight paths. Closed-loop simulation reaffirmed this statement in the presence of realistic aircraft, sensor, and thermal dynamics. Further, the algorithm was embedded into the ALOFT soaring platform (a 4.3m wingspan unmanned glider) for flight testing, which demonstrated its ability to track real-world thermals during cross-country flights exceeding 5 hours flight time over a 70 mile course.en_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectautonomous soaringen_US
dc.subjectatmospheric energyen_US
dc.titleUnscented Kalman Filtering for Real-Time Atmospheric Thermal Trackingen_US


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