東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Network of Unmanned Surface Vehicles: Design and Application to Target Tracking.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Network of Unmanned Surface Vehicles: Design and Application to Target Tracking./
作者:	Panetta, Chandler J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	66 p.
附註:	Source: Masters Abstracts International, Volume: 82-08.
Contained By:	Masters Abstracts International82-08.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28315150
ISBN:	9798569911264

Network of Unmanned Surface Vehicles: Design and Application to Target Tracking.
Panetta, Chandler J.

Network of Unmanned Surface Vehicles: Design and Application to Target Tracking. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 66 p.

Source: Masters Abstracts International, Volume: 82-08.

Thesis (M.S.)--Michigan State University, 2021.

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

Unmanned surface vehicles (USVs) have gained increased attention in environmental monitoring, navigation assistance, search-and-rescue, and other fields over the past three decades. USVs provide an effective platform for mobile sensing applications and offer flexibility in specific capabilities. This work presents a network of compact USVs that are capable of deploying underwater sensors using an automated winch. The small, maneuverable nature of each USV is ideal for inland bodies of water, and the relatively large payload capacity allows for surveys that last multiple hours. Motivated by the application of acoustic telemetry-based fish movement tracking, this work focuses on using a network of USVs to localize an underwater acoustic tag by exploiting the time-difference-of-arrival (TDOA) of the emitted signal. A distributed TDOA-based particle filter (PF) algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). Furthermore, an online model learning method is explored, where target position estimates are used to update the unknown probability distributions of the target's movement model. Through numerical simulations, the distributed PF is shown to result in effective estimation of the target position when a node is connected to a network that collectively has an adequate number of TDOA measurements. Additionally, the efficacy of online model learning in handling model uncertainties is demonstrated in simulation studies.TDOA-based localization algorithms are further validated in field experiments using a network of four USVs carrying acoustic telemetry equipment. In particular, TDOA and GPS data are collected and used to assess the target estimation performance for the distributed TDOA-based PF and a distributed TDOA-based extended Kalman filter (EKF) under different settings for the network topology.

ISBN: 9798569911264Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Localization

Network of Unmanned Surface Vehicles: Design and Application to Target Tracking.
LDR:03336nmm a2200493 4500 001 2342105
005 20211209144643.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798569911264
035 $a (MiAaPQ)AAI28315150
035 $a AAI28315150
040 $a MiAaPQ $c MiAaPQ
100 1 $a Panetta, Chandler J. $0 (orcid)0000-0002-1349-0745 $3 3680410
245 1 0 $a Network of Unmanned Surface Vehicles: Design and Application to Target Tracking.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 66 p.
500 $a Source: Masters Abstracts International, Volume: 82-08.
500 $a Advisor: Tan, Xiaobo.
502 $a Thesis (M.S.)--Michigan State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Unmanned surface vehicles (USVs) have gained increased attention in environmental monitoring, navigation assistance, search-and-rescue, and other fields over the past three decades. USVs provide an effective platform for mobile sensing applications and offer flexibility in specific capabilities. This work presents a network of compact USVs that are capable of deploying underwater sensors using an automated winch. The small, maneuverable nature of each USV is ideal for inland bodies of water, and the relatively large payload capacity allows for surveys that last multiple hours. Motivated by the application of acoustic telemetry-based fish movement tracking, this work focuses on using a network of USVs to localize an underwater acoustic tag by exploiting the time-difference-of-arrival (TDOA) of the emitted signal. A distributed TDOA-based particle filter (PF) algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). Furthermore, an online model learning method is explored, where target position estimates are used to update the unknown probability distributions of the target's movement model. Through numerical simulations, the distributed PF is shown to result in effective estimation of the target position when a node is connected to a network that collectively has an adequate number of TDOA measurements. Additionally, the efficacy of online model learning in handling model uncertainties is demonstrated in simulation studies.TDOA-based localization algorithms are further validated in field experiments using a network of four USVs carrying acoustic telemetry equipment. In particular, TDOA and GPS data are collected and used to assess the target estimation performance for the distributed TDOA-based PF and a distributed TDOA-based extended Kalman filter (EKF) under different settings for the network topology.
590 $a School code: 0128.
650 4 $a Electrical engineering. $3 649834
650 4 $a Robotics. $3 519753
650 4 $a Remote sensing. $3 535394
650 4 $a Computer science. $3 523869
650 4 $a Technical communication. $3 3172863
650 4 $a Artificial intelligence. $3 516317
650 4 $a Operations research. $3 547123
650 4 $a Mechanical engineering. $3 649730
653 $a Localization
653 $a Particle filter
653 $a TDOA
653 $a Unmanned surface vehicles (USVs)
653 $a Inland bodies of water
653 $a Payload capacity
653 $a Moving target
653 $a Environmental monitoring technologies
653 $a Navigation assistance
653 $a Search-and-rescue technology
690 $a 0544
690 $a 0771
690 $a 0796
690 $a 0984
690 $a 0643
690 $a 0800
690 $a 0548
690 $a 0799
710 2 $a Michigan State University. $b Electrical Engineering - Master of Science. $3 3172054
773 0 $t Masters Abstracts International $g 82-08.
790 $a 0128
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28315150