Paper Info
Title
Three dimensional compressed sensing for wireless networks‐based multiple node localization in multi‐floor buildings
Abstract
In wireless network-based node localization, the received signals are hampered by complex phenomena, such as shadowing, noise, and multi-path fading. In this work, the localization is stated as an ill-posed problem that can be solved by compressed sensing (CS) technique. A three dimensional (3D)-CS approach using the ratio of received signal strength (R2S2) and the time difference of arrival metrics was proposed to improve the localization accuracy of multiple target nodes in 3D wireless networks, and to reduce deployment complexity and processing time. Simulation and experimental tests were conducted in a large multi-floors building using the strength of the received signals and the radio map of the localization area. The results indicated that the 3D-CS approach is reliable for identifying the floor number and estimating the horizontal position. The localization precision is less affected by the propagation medium variation than the conventional 2D-CS method. The localization mean error is lower when the number of access points increases, and the radio map spacing decreases. In addition, the accuracy of the 3D-CS approach was assured as well as the building material characteristics, position of access points, and wireless-terminal real transmission power are unknown. Copyright (c) 2015 John Wiley & Sons, Ltd.
Year
DOI
Venue
2016
10.1002/wcm.2653
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
Keywords
Field
DocType
compressed sensing,localization in 3D WNs,indoor positioning,RTLS
Wireless network,Telecommunications,Fading,Computer science,Mean squared error,Real-time computing,Horizontal position representation,Signal strength,Multilateration,Real-time locating system,Compressed sensing,Distributed computing
Journal
Volume
Issue
ISSN
16.0
13.0
1530-8669
Citations 
PageRank 
References 
0
0.34
16
Authors
2
Name
Order
Citations
PageRank
mohamed abid100.34
Soumaya Cherkaoui218740.89