ERLAK: On the Cooperative Estimation of the Real-Time RSSI Based Location and K Constant Term

ERLAK: On the Cooperative Estimation of the Real-Time RSSI Based Location and K Constant Term Nowadays location estimation using WiFi networks in indoor environments has become a hot research topic. Challenging methods without calibration or hardware integration are essentially required for cost-effective and practical solutions. The Received Signal Strength Indicator-based localization methods offer low cost solutions. However, their propagation models are difficult to characterize due to environmental factors in indoor and multiple parameters. There are a number of works over estimation of location and pathloss exponent presented in the literature. This paper introduces a new method shortly named as ERLAK in order to estimate the K constant term using log normal channel model in addition to the location of mobile station in indoor environment. The ERLAK method has been consistently compared to the well-known Least Square and Weighted Least Square methods. It achieves the least errors in distance estimations compared to the classical methods on especially critical measurement points. It remarkably accomplishes less than 5 m mean errors for distance estimation results particularly when signal is received from all of the access points. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

ERLAK: On the Cooperative Estimation of the Real-Time RSSI Based Location and K Constant Term

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4032-7
Publisher site
See Article on Publisher Site

Abstract

Nowadays location estimation using WiFi networks in indoor environments has become a hot research topic. Challenging methods without calibration or hardware integration are essentially required for cost-effective and practical solutions. The Received Signal Strength Indicator-based localization methods offer low cost solutions. However, their propagation models are difficult to characterize due to environmental factors in indoor and multiple parameters. There are a number of works over estimation of location and pathloss exponent presented in the literature. This paper introduces a new method shortly named as ERLAK in order to estimate the K constant term using log normal channel model in addition to the location of mobile station in indoor environment. The ERLAK method has been consistently compared to the well-known Least Square and Weighted Least Square methods. It achieves the least errors in distance estimations compared to the classical methods on especially critical measurement points. It remarkably accomplishes less than 5 m mean errors for distance estimation results particularly when signal is received from all of the access points.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: Feb 8, 2017

References

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