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Drone Forensics: Framework for Paylo...

Ryan, Justin.

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Drone Forensics: Framework for Payload Detection Through Flight Log Analysis of Dji Mavic 2 Zoom.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Drone Forensics: Framework for Payload Detection Through Flight Log Analysis of Dji Mavic 2 Zoom./
作者:	Ryan, Justin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	58 p.
附註:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30485228
ISBN:	9798379571726

Drone Forensics: Framework for Payload Detection Through Flight Log Analysis of Dji Mavic 2 Zoom.
Ryan, Justin.

Drone Forensics: Framework for Payload Detection Through Flight Log Analysis of Dji Mavic 2 Zoom. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 58 p.

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.)--University of Colorado at Denver, 2023.

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

This thesis's goal is to lay the groundwork for a method of analyzing flight records of consumer DJI drones to detect the presence of external payload, specifically by comparing motor RPM and motor amperage of payload vs. non-payload flights (wet vs dry). Current research in Drone flight data analysis is limited, and generally concerns itself with the controller-drone relationship, GPS, and geofencing data. This research is, to our knowledge, the first hands-on test of what has only been a theory in the Drone Forensic community. A base understanding of aerodynamics and electrical engineering allows for the hypothesis that if a drone is laden with an external payload, then the data fields that record such events as motor rpm and motor amperage will show an increased output as the airfoils are required to generate more lift. This research is intended to be a framework for future research showing proof of concept. In a controlled environment with a DJI Mavic 2 Zoom at hover, base measurements are taken for the drone in natural unloaded flight (dry) and then compared to the same flight performed with a payload attached (wet). The results are clear; with a significant payload attached to the drone, the automated systems will be forced to generate more lift by increasing motor RPM and Battery Current; this information is recorded in the Flight Records .DAT file that is stored in the controller application. Side by side analysis of the dry-wet data shows a clear picture of which drone was carrying the payload. Future research must look to advance this procedure for real-life events where a drone is not held at a simple hover but is maneuvering in different directions in outdoor conditions where variability in atmosphere, wind, and pilot input are not controlled. 

ISBN: 9798379571726Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

DJI

Drone Forensics: Framework for Payload Detection Through Flight Log Analysis of Dji Mavic 2 Zoom.
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500 $a Source: Masters Abstracts International, Volume: 84-11.
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506 $a This item must not be sold to any third party vendors.
520 $a This thesis's goal is to lay the groundwork for a method of analyzing flight records of consumer DJI drones to detect the presence of external payload, specifically by comparing motor RPM and motor amperage of payload vs. non-payload flights (wet vs dry). Current research in Drone flight data analysis is limited, and generally concerns itself with the controller-drone relationship, GPS, and geofencing data. This research is, to our knowledge, the first hands-on test of what has only been a theory in the Drone Forensic community. A base understanding of aerodynamics and electrical engineering allows for the hypothesis that if a drone is laden with an external payload, then the data fields that record such events as motor rpm and motor amperage will show an increased output as the airfoils are required to generate more lift. This research is intended to be a framework for future research showing proof of concept. In a controlled environment with a DJI Mavic 2 Zoom at hover, base measurements are taken for the drone in natural unloaded flight (dry) and then compared to the same flight performed with a payload attached (wet). The results are clear; with a significant payload attached to the drone, the automated systems will be forced to generate more lift by increasing motor RPM and Battery Current; this information is recorded in the Flight Records .DAT file that is stored in the controller application. Side by side analysis of the dry-wet data shows a clear picture of which drone was carrying the payload. Future research must look to advance this procedure for real-life events where a drone is not held at a simple hover but is maneuvering in different directions in outdoor conditions where variability in atmosphere, wind, and pilot input are not controlled. 
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