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Regression Analysis for Estimated Distance in Fingerprinting-Based WLAN Outdoor Localization System by Risanuri Hidayat; I Wayan Mustika is a scholarly article available to read on EtoBox.

What is Regression Analysis for Estimated Distance in Fingerprinting-Based WLAN Outdoor Localization System about?

Wireless local area network (WLAN) localization techniques are evolving in line with technological developments and the number of wireless device users. The existing localization techniques have several methods, with varying degrees of accuracy, and are generally applied to indoors. The targets sought in existing localization techniques find positions of user's mobile device. In this paper describes the regression analysis method for fingerprinting-based WLAN outdoor localization system. The position being searched is the location of the access point, rather than the user's mobile device like any other localization research. With a signal fingerprinting system, the empirical data obtained from field measurements are stored in the database. The database consists of a DataPoint table, which includes received signal strength by the finder (RSSfnd) and the distance between the finder against the access point (Dreal). Measurements were made at a range of 0 to 100 meters and divided into eleven measurement points. Regression models used for analysis are linear regression, exponential regression, and polynomial regression. Based on the regression line and the value of R 2 can conclude the

Author

Risanuri Hidayat; I Wayan Mustika

Published

2018

Language

EN