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Radar Detection, Tracking and Identi...

Moore, Erik G.

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Radar Detection, Tracking and Identification for UAV Sense and Avoid Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Radar Detection, Tracking and Identification for UAV Sense and Avoid Applications./
作者:	Moore, Erik G.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	211 p.
附註:	Source: Masters Abstracts International, Volume: 80-11.
Contained By:	Masters Abstracts International80-11.
標題:	Aerospace engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13808039
ISBN:	9781392158807

Radar Detection, Tracking and Identification for UAV Sense and Avoid Applications.
Moore, Erik G.

Radar Detection, Tracking and Identification for UAV Sense and Avoid Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 211 p.

Source: Masters Abstracts International, Volume: 80-11.

Thesis (M.S.)--University of Denver, 2019.

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

Advances in Unmanned Aerial Vehicle (UAV) technology have enabled wider access for the general public leading to more stringent flight regulations, such as the "line of sight" restriction, for hobbyists and commercial applications. Improving sensor technology for Sense And Avoid (SAA) systems is currently a major research area in the unmanned vehicle community. This thesis overviews efforts made to advance intelligent algorithms used to detect, track, and identify commercial UAV targets by enabling rapid prototyping of novel radar techniques such as micro-Doppler radar target identification or cognitive radar. To enable empirical radar signal processing evaluations, an S-Band and X-Band frequency modulated, software-defined radar testbed is designed, implemented, and evaluated with field measurements. The final evaluations provide proof of functionality, performance measurements, and limitations of this testbed and future software-defined radars. The testbed is comprised of open-source software and hardware meant to accelerate the development of a reliable, repeatable, and scalable SAA system for the wide range of new and existing UAVs.

ISBN: 9781392158807Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Micro Doppler

Radar Detection, Tracking and Identification for UAV Sense and Avoid Applications.
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300 $a 211 p.
500 $a Source: Masters Abstracts International, Volume: 80-11.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Rutherford, Matt J.;Valavanis, Kimon P.
502 $a Thesis (M.S.)--University of Denver, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Advances in Unmanned Aerial Vehicle (UAV) technology have enabled wider access for the general public leading to more stringent flight regulations, such as the "line of sight" restriction, for hobbyists and commercial applications. Improving sensor technology for Sense And Avoid (SAA) systems is currently a major research area in the unmanned vehicle community. This thesis overviews efforts made to advance intelligent algorithms used to detect, track, and identify commercial UAV targets by enabling rapid prototyping of novel radar techniques such as micro-Doppler radar target identification or cognitive radar. To enable empirical radar signal processing evaluations, an S-Band and X-Band frequency modulated, software-defined radar testbed is designed, implemented, and evaluated with field measurements. The final evaluations provide proof of functionality, performance measurements, and limitations of this testbed and future software-defined radars. The testbed is comprised of open-source software and hardware meant to accelerate the development of a reliable, repeatable, and scalable SAA system for the wide range of new and existing UAVs.
590 $a School code: 0061.
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Electrical engineering. $3 649834
650 4 $a Electromagnetics. $3 3173223
653 $a Micro Doppler
653 $a Radar
653 $a Sense and avoid
653 $a Software defined radar
653 $a Software defined radio
653 $a Unmanned aerial systems
690 $a 0538
690 $a 0544
690 $a 0607
710 2 $a University of Denver. $b Electrical Engineering. $3 2102324
773 0 $t Masters Abstracts International $g 80-11.
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791 $a M.S.
792 $a 2019
793 $a English
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