國立虎尾科技大學 |

Classifying Aerial Objects from Trajectory Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Classifying Aerial Objects from Trajectory Data./
作者:	Dihel, Logan Thomas.
面頁冊數:	1 online resource (96 pages)
附註:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
標題:	Aerospace engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798379904777

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

Classifying Aerial Objects from Trajectory Data. - 1 online resource (96 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379904777Subjects--Topical Terms:

686400
Aerospace engineering.
Subjects--Index Terms:

Aerial objectsIndex Terms--Genre/Form:

554714
Electronic books.

Classifying Aerial Objects from Trajectory Data.
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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.
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653 $a Bird-drone problem
653 $a Trajectory data
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