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Classifying Aerial Objects from Traj...

Dihel, Logan Thomas.

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Classifying Aerial Objects from Trajectory Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Classifying Aerial Objects from Trajectory Data./
Author:	Dihel, Logan Thomas.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	96 p.
Notes:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
Subject:	Electrical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30317898
ISBN:	9798379904777

Classifying Aerial Objects from Trajectory Data.
Dihel, Logan Thomas.

Classifying Aerial Objects from Trajectory Data. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 96 p.

Source: Masters Abstracts International, Volume: 85-01.

Thesis (M.S.)--Washington State University, 2023.

This item must not be sold to any third party vendors.

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.

ISBN: 9798379904777Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Aerial objects

Classifying Aerial Objects from Trajectory Data.
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100 1 $a Dihel, Logan Thomas. $0 (orcid)0009-0000-4935-561X $3 3766935
245 1 0 $a Classifying Aerial Objects from Trajectory Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 96 p.
500 $a Source: Masters Abstracts International, Volume: 85-01.
500 $a Advisor: Roy, Sandip.
502 $a Thesis (M.S.)--Washington State University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
590 $a School code: 0251.
650 4 $a Electrical engineering. $3 649834
650 4 $a Remote sensing. $3 535394
650 4 $a Aerospace engineering. $3 1002622
653 $a Aerial objects
653 $a Consumer-grade drones
653 $a Drones
653 $a Bird-drone problem
653 $a Trajectory data
690 $a 0544
690 $a 0799
690 $a 0538
710 2 $a Washington State University. $b School of Electrical Engineering and Computer Science. $3 3764368
773 0 $t Masters Abstracts International $g 85-01.
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791 $a M.S.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30317898