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Wireless Sensor Localization With Di...

Wang, Jing.

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Wireless Sensor Localization With Distance Measurement Information.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Wireless Sensor Localization With Distance Measurement Information./
作者:	Wang, Jing.
面頁冊數:	120 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 2333.
Contained By:	Dissertation Abstracts International72-04B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3439983
ISBN:	9781124460529

Wireless Sensor Localization With Distance Measurement Information.
Wang, Jing.

Wireless Sensor Localization With Distance Measurement Information. - 120 p.

Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 2333.

Thesis (Ph.D.)--The Catholic University of America, 2011.

The dissertation would focus on two problems: localization in wireless sensor network and distance reconstruction. Sensors are often randomly deployed and in such case, determining each sensor's position is critical, which explains the recent attention given to the sensor localization problem. Previous methods of projection onto convex sets (POCS) overcome the multimodality problem that plagues earlier least-squares formulations.

ISBN: 9781124460529Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Wireless Sensor Localization With Distance Measurement Information.
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500 $a Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 2333.
500 $a Adviser: Phillip A. Regalia.
502 $a Thesis (Ph.D.)--The Catholic University of America, 2011.
520 $a The dissertation would focus on two problems: localization in wireless sensor network and distance reconstruction. Sensors are often randomly deployed and in such case, determining each sensor's position is critical, which explains the recent attention given to the sensor localization problem. Previous methods of projection onto convex sets (POCS) overcome the multimodality problem that plagues earlier least-squares formulations.
520 $a Previous efforts in this direction require that the sensor be located in certain position in the plane. Here we propose a new algorithm which projects onto the boundary of convex sets, and features a computationally simple update procedure. The new algorithm has a unique solution no matter where the sensor is located at. Simulation results will be shown and conclusions would be given.
520 $a Sensor localization typically exploits distance measurements to infer sensor positions with respect to known anchor nodes. Missing or unreliable measurements for special nodes can impede such procedures, raising the problem of distance measurement reconstruction using distance information from other nodes. The problem has traditionally been approached through multidimensional scaling, and more recently through semidenite programming and low-rank matrix completion. Here we develop new iterative reconstruction algorithms which instead exploit inertia of key matrices, thereby encompassing stronger constraints than rank alone. This serves to overcome limitations observed in earlier competitive approaches which do not exploit the problem structure as well. Simulation examples illustrate the performance of the new algorithms.
590 $a School code: 0043.
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0544
710 2 $a The Catholic University of America. $b Electrical Engineering and Computer Science. $3 1682582
773 0 $t Dissertation Abstracts International $g 72-04B.
790 1 0 $a Regalia, Phillip A., $e advisor
790 1 0 $a Namazi, Nader M. $e committee member
790 1 0 $a Kilic, Ozlem $e committee member
790 $a 0043
791 $a Ph.D.
792 $a 2011
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