Error Data Analytics on RSS Range-Based Localization
The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods. Due to the inevitable measurement error, the analytics on the error data is critical to evaluate localization methods and to find the effective ones. For indoor localization, Received Signal...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2020-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020001 |
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| author | Shuhui Yang Zimu Yuan Wei Li |
| author_facet | Shuhui Yang Zimu Yuan Wei Li |
| author_sort | Shuhui Yang |
| collection | DOAJ |
| description | The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods. Due to the inevitable measurement error, the analytics on the error data is critical to evaluate localization methods and to find the effective ones. For indoor localization, Received Signal Strength (RSS) is a convenient and low-cost measurement that has been adopted in many localization approaches. However, using RSS data for localization needs to solve a fundamental problem, that is, how accurate are these methods? The reason of the low accuracy of the current RSS-based localization methods is the oversimplified analysis on RSS measurement data. In this proposed work, we adopt a generalized measurement model to find optimal estimators whose estimated error is equal to the Cramér-Rao Lower Bound (CRLB). Through mathematical techniques, the key factors that affect the accuracy of RSS-based localization methods are revealed, and the analytics expression that discloses the proportional relationship between the localization accuracy and these factors is derived. The significance of our discovery has two folds: First, we present a general expression for localization error data analytics, which can explain and predict the accuracy of range-based localization algorithms; second, the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms. |
| format | Article |
| id | doaj-art-ea9ee9b718f74333ac4e7a56bf1560bc |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-ea9ee9b718f74333ac4e7a56bf1560bc2025-02-02T23:47:57ZengTsinghua University PressBig Data Mining and Analytics2096-06542020-09-013315517010.26599/BDMA.2020.9020001Error Data Analytics on RSS Range-Based LocalizationShuhui Yang0Zimu Yuan1Wei Li2<institution content-type="dept">Department of Mathematics, Statistics, and Computer Science</institution>, <institution>Purdue University Northwest</institution>, <city>Hammond</city>, <state>IN</state> <postal-code>46323</postal-code>, <country>USA</country>.<institution content-type="dept">Institute of Information Engineering</institution>, <institution>Chinese Academy of Sciences</institution>, <city>Beijing</city> <postal-code>100864</postal-code>, <country>China</country>.<institution content-type="dept">Institute of Computing Technology</institution>, <institution>Chinese Academy of Sciences</institution>, <city>Beijing</city> <postal-code>100864</postal-code>, <country>China</country>.The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods. Due to the inevitable measurement error, the analytics on the error data is critical to evaluate localization methods and to find the effective ones. For indoor localization, Received Signal Strength (RSS) is a convenient and low-cost measurement that has been adopted in many localization approaches. However, using RSS data for localization needs to solve a fundamental problem, that is, how accurate are these methods? The reason of the low accuracy of the current RSS-based localization methods is the oversimplified analysis on RSS measurement data. In this proposed work, we adopt a generalized measurement model to find optimal estimators whose estimated error is equal to the Cramér-Rao Lower Bound (CRLB). Through mathematical techniques, the key factors that affect the accuracy of RSS-based localization methods are revealed, and the analytics expression that discloses the proportional relationship between the localization accuracy and these factors is derived. The significance of our discovery has two folds: First, we present a general expression for localization error data analytics, which can explain and predict the accuracy of range-based localization algorithms; second, the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.https://www.sciopen.com/article/10.26599/BDMA.2020.9020001cramér-rao lower bound (crlb)error data analyticsgeneralized least squaresreceived signal strength (rss) |
| spellingShingle | Shuhui Yang Zimu Yuan Wei Li Error Data Analytics on RSS Range-Based Localization Big Data Mining and Analytics cramér-rao lower bound (crlb) error data analytics generalized least squares received signal strength (rss) |
| title | Error Data Analytics on RSS Range-Based Localization |
| title_full | Error Data Analytics on RSS Range-Based Localization |
| title_fullStr | Error Data Analytics on RSS Range-Based Localization |
| title_full_unstemmed | Error Data Analytics on RSS Range-Based Localization |
| title_short | Error Data Analytics on RSS Range-Based Localization |
| title_sort | error data analytics on rss range based localization |
| topic | cramér-rao lower bound (crlb) error data analytics generalized least squares received signal strength (rss) |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020001 |
| work_keys_str_mv | AT shuhuiyang errordataanalyticsonrssrangebasedlocalization AT zimuyuan errordataanalyticsonrssrangebasedlocalization AT weili errordataanalyticsonrssrangebasedlocalization |