Risk-based supervisory guidance for detect and avoid involving small unmanned aircraft systems

Fang, Scott Xiang (2018) Risk-based supervisory guidance for detect and avoid involving small unmanned aircraft systems. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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A formidable barrier for small Unmanned Aircraft Systems (UAS) to be integrated into civil airspace is that small UAS currently lack the ability to Detect and Avoid (DAA) other aircraft during ight operations; however, this ability is an essential part of regulations governing the general operation of aircraft in civil airspace. In this way, the research described is focused on achieving an equivalent level of safety for small UAS as manned aircraft in civil airspace. A small UAS DAA system was proposed to guide small UAS to detect nearby traffic, identify hazards, assess collision risks, perform mitigation analyses, and choose appropriate maneuvers to avoid potential collisions in mid-air encounters. To facilitate system development and performance evaluation, the proposed DAA system was designed and implemented on a fast-time simulation-based analysis platform, on which a set of quantifiable analysis metrics were designed for small UAS to improve situation awareness in hazard identification and collision risk assessment; and a learning-based Smart Decision Tree Method (SDTM) was developed to provide real-time supervisory DAA guidance for small UAS to avoid potential collisions in mitigation analysis. The theoretical research achieved was also integrated into an effort to implement an Automatic Collision Avoidance System (ACAS) to verify the short range DAA performance for small UAS in the visual-line-of-sight ight tests performed at the RAVEN test site in Argentia, NL.

Item Type: Thesis (Doctoral (PhD))
URI: http://research.library.mun.ca/id/eprint/13204
Item ID: 13204
Additional Information: Includes bibliographical references (pages 169-190).
Keywords: small unmanned aircraft systems, detect and avoid, collision hazard identification, collision risk assessment, collision mitigation guidance
Department(s): Engineering and Applied Science, Faculty of
Date: March 2018
Date Type: Submission
Library of Congress Subject Heading: Drone aircraft -- Remote sensing; Airplanes -- Collision avoidance

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