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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.


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Error Data Analytics on RSS Range-Based Localization

Show Author's information Shuhui Yang( )Zimu YuanWei Li
Department of Mathematics, Statistics, and Computer Science, Purdue University Northwest, Hammond, IN 46323, USA.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100864, China.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100864, China.

Abstract

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.

Keywords:

Cramér-Rao Lower Bound (CRLB), error data analytics, generalized least squares, Received Signal Strength (RSS)
Received: 31 January 2020 Accepted: 07 February 2020 Published: 16 July 2020 Issue date: September 2020
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Publication history

Received: 31 January 2020
Accepted: 07 February 2020
Published: 16 July 2020
Issue date: September 2020

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© The author(s) 2020

Acknowledgements

This work was partially supported by the National Key Research and Development Program of China (No. 2016YFE0121800).

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