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DTSTART;TZID="Pacific Time (US & Canada)":20230414T090000
DTEND;TZID="Pacific Time (US & Canada)":20230414T110000
SUMMARY:EECS – Thesis Defense: Classifying Aerial Objects From Trajectory Data, Logan Dihel
LOCATION:Electrical and Mechanical Engineering Building
DESCRIPTION:Student: Logan Dihel\n\nAdvisor: Dr. Sandip Roy\n\nDegree: Electrical Engineering MS\n\nThesis Title: Classifying Aerial Objects from Trajectory Data\n\nAbstract: The recent availability of consumer-grade drones has dramatically increased the number of unmanned aerial systems piloted in the United States. Unfortunately, this has resulted in operators using drones with malicious intent, including smuggling contraband into federal prisons. Because of this, there have been wide-spread efforts from researchers to develop technologies which can detect and classify aerial objects, including drones. A key challenge of aerial object classification is differentiating between birds and drones, which is known as the bird-drone problem. Birds and drones are difficult to distinguish because of their similar size and velocities. Previous researchers have used a combination of image-based machine learning, radar cross sections, and acoustic methods to solve the bird-drone problem, with varying degrees of success. An alternative, less researched methodology considers classifying aerial objects from trajectory data, which exploits the fundamental differences between the flight patterns in birds and drones. This thesis is a collection of works which develop technology aiming to classify aerial objects from trajectory data.
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