VISION-BASED RELATIVE POSITION ESTIMATION AND INTERCEPT TRAJECTORY PLANNING FOR SMALL UNMANNED AIRCRAFT SYSTEMS
dc.contributor.advisor | Yakimenko, Oleg A. | |
dc.contributor.author | Tan, Kang Hao | |
dc.contributor.department | Systems Engineering (SE) | |
dc.contributor.secondreader | Papoulias, Fotis A. | |
dc.date.accessioned | 2019-11-04T18:20:26Z | |
dc.date.available | 2019-11-04T18:20:26Z | |
dc.date.issued | 2019-09 | |
dc.description.abstract | The proliferation of unmanned aircraft systems (UAS) has contributed to the asymmetric threat of malevolent actors exploiting this technology for mischief or harm. Existing ground-based solutions are limited by line of sight, while human-operated responder drones can be less responsive and more labor-intensive. Hence, there is a capability requirement for autonomous vision-based pursuit and interception of unauthorized drones. To address this, the author developed a computer vision (CV) algorithm to detect, track and estimate the relative position and range of a hovering and moving airborne small UAS target in field conditions. CV-based measurements were compared against GPS data, to assess the range and angular estimation performance of the CV algorithm. Then, the CV-estimated range and angular information was processed by a flight control algorithm utilizing simple angular guidance principle to pursue and intercept the target. Field tests of the algorithm were done using a prototype drone. This research will inform the conceptual design and choice of hardware implementation for a commercial-off-the-shelf-based counter-UAS capability. More broadly, the research contributes to the body of knowledge in autonomous object tracking applications. | en_US |
dc.description.distributionstatement | Approved for public release; distribution is unlimited. | |
dc.description.recognition | Outstanding Thesis | en_US |
dc.description.service | Major, Republic of Singapore Air Force | en_US |
dc.description.uri | http://archive.org/details/visionbasedrelat1094563509 | |
dc.identifier.thesisid | 32725 | |
dc.identifier.uri | https://hdl.handle.net/10945/63509 | |
dc.publisher | Monterey, CA; Naval Postgraduate School | en_US |
dc.relation.ispartofseries | NPS Outstanding Theses and Dissertations | |
dc.rights | Copyright is reserved by the copyright owner. | en_US |
dc.subject.author | computer vision | en_US |
dc.subject.author | object detection | en_US |
dc.subject.author | object tracking | en_US |
dc.subject.author | position estimation | en_US |
dc.subject.author | navigation | en_US |
dc.subject.author | trajectory | en_US |
dc.subject.author | UAS | en_US |
dc.subject.author | drone | en_US |
dc.title | VISION-BASED RELATIVE POSITION ESTIMATION AND INTERCEPT TRAJECTORY PLANNING FOR SMALL UNMANNED AIRCRAFT SYSTEMS | en_US |
dc.type | Thesis | en_US |
dspace.entity.type | Publication | |
etd.thesisdegree.discipline | Systems Engineering | en_US |
etd.thesisdegree.grantor | Naval Postgraduate School | en_US |
etd.thesisdegree.level | Masters | en_US |
etd.thesisdegree.name | Master of Science in Systems Engineering | en_US |
relation.isSeriesOfPublication | c5e66392-520c-4aaf-9b4f-370ce82b601f | |
relation.isSeriesOfPublication.latestForDiscovery | c5e66392-520c-4aaf-9b4f-370ce82b601f |
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