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Deep Learning Based Passive WiFi Loc...

Shi, Hao.

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Deep Learning Based Passive WiFi Localization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning Based Passive WiFi Localization./
Author:	Shi, Hao.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	35 p.
Notes:	Source: Masters Abstracts International, Volume: 82-05.
Contained By:	Masters Abstracts International82-05.
Subject:	Artificial intelligence. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28089956
ISBN:	9798678124210

Deep Learning Based Passive WiFi Localization.
Shi, Hao.

Deep Learning Based Passive WiFi Localization. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 35 p.

Source: Masters Abstracts International, Volume: 82-05.

Thesis (M.S.)--State University of New York at Buffalo, 2020.

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

Passive WiFi localization works by capturing and analyzing the signals that reflected from human body without any devices attached to human. Although physical model has been built in former research to get relationship between signal and location, it requires accurate measurement of WiFi antenna set up. Parameters like angle-of arrival(AoA) and time-of-flight(ToF) are impossible to be estimated accurately in some cases. This paper presents a device-free WiFi localization system, which uses a combination of physical model and deep learning method to estimate human location. Firstly, compare to traditional AoA and ToF estimation, we propose a modified signal profile inherits the spirit of AoA ToF profile but overcomes the limit of accurate measurement of antenna distance. Then, we create Gaussian distribution image based on these signal profile and use deep learning to learn the relationship between the profile and ground truth location. Our system achieves accurate human localization without knowledge of accurate deployment of the WiFi system.

ISBN: 9798678124210Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Deep learning

Deep Learning Based Passive WiFi Localization.
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245 1 0 $a Deep Learning Based Passive WiFi Localization.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 35 p.
500 $a Source: Masters Abstracts International, Volume: 82-05.
500 $a Advisor: Su, Lu.
502 $a Thesis (M.S.)--State University of New York at Buffalo, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Passive WiFi localization works by capturing and analyzing the signals that reflected from human body without any devices attached to human. Although physical model has been built in former research to get relationship between signal and location, it requires accurate measurement of WiFi antenna set up. Parameters like angle-of arrival(AoA) and time-of-flight(ToF) are impossible to be estimated accurately in some cases. This paper presents a device-free WiFi localization system, which uses a combination of physical model and deep learning method to estimate human location. Firstly, compare to traditional AoA and ToF estimation, we propose a modified signal profile inherits the spirit of AoA ToF profile but overcomes the limit of accurate measurement of antenna distance. Then, we create Gaussian distribution image based on these signal profile and use deep learning to learn the relationship between the profile and ground truth location. Our system achieves accurate human localization without knowledge of accurate deployment of the WiFi system.
590 $a School code: 0656.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
650 4 $a Remote sensing. $3 535394
650 4 $a Geotechnology. $3 1018558
653 $a Deep learning
653 $a Passive localization
653 $a Signal processing
653 $a WiFi localization
653 $a Remote devices
653 $a Location technology
653 $a Gaussian distribution
653 $a Ground truth location
690 $a 0800
690 $a 0984
690 $a 0799
690 $a 0428
710 2 $a State University of New York at Buffalo. $b Computer Science and Engineering. $3 1035503
773 0 $t Masters Abstracts International $g 82-05.
790 $a 0656
791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28089956