Date of Award

January 2012

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Ronald A. Marsh

Abstract

The Ganged Phased Array Radar - Risk Mitigation System (GPAR-RMS) was a

mobile ground-based sense-and-avoid system for Unmanned Aircraft System (UAS)

operations developed by the University of North Dakota. GPAR-RMS detected proximate

aircraft with various sensor systems, including a 2D radar and an Automatic Dependent

Surveillance - Broadcast (ADS-B) receiver. Information about those aircraft was then

displayed to UAS operators via visualization software developed by the University of

North Dakota. The Risk Mitigation (RM) subsystem for GPAR-RMS was designed to

estimate the current risk of midair collision, between the Unmanned Aircraft (UA) and a

General Aviation (GA) aircraft flying under Visual Flight Rules (VFR) in the surrounding

airspace, for UAS operations in Class E airspace (i.e. below 18,000 feet MSL). However,

accurate probabilistic models for the behavior of pilots of GA aircraft flying under VFR

in Class E airspace were needed before the RM subsystem could be implemented.

In this dissertation the author presents the results of data mining an aircraft

telemetry data set from a consecutive nine month period in 2011. This aircraft telemetry

data set consisted of Flight Data Monitoring (FDM) data obtained from Garmin G1000

devices onboard every Cessna 172 in the University of North Dakota's training fleet.

Data from aircraft which were potentially within the controlled airspace surrounding

controlled airports were excluded. Also, GA aircraft in the FDM data flying in Class E

airspace were assumed to be flying under VFR, which is usually a valid assumption.

Complex subpaths were discovered from the aircraft telemetry data set using a novel

application of an ant colony algorithm. Then, probabilistic models were data mined from

those subpaths using extensions of the Genetic K-Means (GKA) and Expectation-

Maximization (EM) algorithms.

The results obtained from the subpath discovery and data mining suggest a pilot

flying a GA aircraft near to an uncontrolled airport will perform different maneuvers than

a pilot flying a GA aircraft far from an uncontrolled airport, irrespective of the altitude of

the GA aircraft. However, since only aircraft telemetry data from the University of North

Dakota's training fleet were data mined, these results are not likely to be applicable to GA

aircraft operating in a non-training environment.

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