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.
","abstract_html":"<p>The Ganged Phased Array Radar - Risk Mitigation System (GPAR-RMS) was a</p> <p>mobile ground-based sense-and-avoid system for Unmanned Aircraft System (UAS)</p> <p>operations developed by the University of North Dakota. GPAR-RMS detected proximate</p> <p>aircraft with various sensor systems, including a 2D radar and an Automatic Dependent</p> <p>Surveillance - Broadcast (ADS-B) receiver. Information about those aircraft was then</p> <p>displayed to UAS operators via visualization software developed by the University of</p> <p>North Dakota. The Risk Mitigation (RM) subsystem for GPAR-RMS was designed to</p> <p>estimate the current risk of midair collision, between the Unmanned Aircraft (UA) and a</p> <p>General Aviation (GA) aircraft flying under Visual Flight Rules (VFR) in the surrounding</p> <p>airspace, for UAS operations in Class E airspace (i.e. below 18,000 feet MSL). However,</p> <p>accurate probabilistic models for the behavior of pilots of GA aircraft flying under VFR</p> <p>in Class E airspace were needed before the RM subsystem could be implemented.</p> <p>In this dissertation the author presents the results of data mining an aircraft</p> <p>telemetry data set from a consecutive nine month period in 2011. This aircraft telemetry</p> <p>data set consisted of Flight Data Monitoring (FDM) data obtained from Garmin G1000</p> <p>devices onboard every Cessna 172 in the University of North Dakota's training fleet.</p> <p>Data from aircraft which were potentially within the controlled airspace surrounding</p> <p>controlled airports were excluded. Also, GA aircraft in the FDM data flying in Class E</p> <p>airspace were assumed to be flying under VFR, which is usually a valid assumption.</p> <p>Complex subpaths were discovered from the aircraft telemetry data set using a novel</p> <p>application of an ant colony algorithm. Then, probabilistic models were data mined from</p> <p>those subpaths using extensions of the Genetic K-Means (GKA) and Expectation-</p> <p>Maximization (EM) algorithms.</p> <p>The results obtained from the subpath discovery and data mining suggest a pilot</p> <p>flying a GA aircraft near to an uncontrolled airport will perform different maneuvers than</p> <p>a pilot flying a GA aircraft far from an uncontrolled airport, irrespective of the altitude of</p> <p>the GA aircraft. However, since only aircraft telemetry data from the University of North</p> <p>Dakota's training fleet were data mined, these results are not likely to be applicable to GA</p> <p>aircraft operating in a non-training environment.</p>","abstract_has_math":false,"creators":["Ogaard, Kirk Anders"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Ronald A. Marsh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-01-01T08:00:00Z","date_published":"2012-01-01T08:00:00Z","updated_at":"2026-07-24T03:26:17Z","subjects":["ant colony algorithms, collision avoidance, data mining, genetic algorithms, K-means algorithm, unmanned aircraft"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.und.edu/theses/1262","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ronald A. Marsh"]},{"key":"dc:creator","label":"Author","values":["Ogaard, Kirk Anders"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ant colony algorithms, collision avoidance, data mining, genetic algorithms, K-means algorithm, unmanned aircraft"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.und.edu/theses/1262"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Mining Aircraft Telemetry Data With Evolutionary Algorithms"]}]}],"canonical_facts":{"dc:contributor":["Ronald A. Marsh"],"dc:creator":["Ogaard, Kirk Anders"],"dc:description.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.
"],"dc:identifier":["https://commons.und.edu/theses/1262"],"dc:subject":["ant colony algorithms, collision avoidance, data mining, genetic algorithms, K-means algorithm, unmanned aircraft"],"dc:title":["Mining Aircraft Telemetry Data With Evolutionary Algorithms"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:26:17Z"}