NDLI: Integrating active and passive received signal strength-based localization

Content Provider	IEEE Xplore Digital Library
Author	Talampas, Marc Caesar R. Low, Kay-Soon
Copyright Year	2014
Description	Author affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore (Talampas, Marc Caesar R.; Low, Kay-Soon)
Abstract	Active received signal strength (RSS)-based localization systems estimate the location of a device-equipped target by using the RSS measurements between the target's device and a set of known-location nodes. To improve the accuracy of such systems, additional information from other devices such as inertial sensors or antenna arrays have been used at the cost of increased power consumption and complexity. Recently, RSS-based device-free localization (DFL) systems have been developed that can estimate a human target's location using only the shadowing caused by the target on the radio links within the network, and without requiring the target to be equipped with a radio device. In this paper, we integrate the active and passive RSS-based localization approaches to estimate the location of a single human target using a maximum likelihood estimation framework. Through an outdoor experiment, we show that the integrated method results in increased localization accuracy as compared to using either active or passive RSS-based localization methods alone and without requiring additional sensors.
Starting Page	153
Ending Page	158
File Size	1220546
Page Count	6
File Format	PDF
ISBN	9781479961207
DOI	10.1109/ISPACS.2014.7024443
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-01
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Attenuation Maximum likelihood estimation Loss measurement Training Area measurement Attenuation measurement Accuracy hybrid localization RSS-based localization device free localization maximum likelihood estimation wireless sensor networks
Content Type	Text
Resource Type	Article

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