A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall locali...

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Bibliographic Details
Published in:IEEE transactions on mobile computing Vol. 16; no. 8; pp. 2079 - 2092
Main Authors: Khalajmehrabadi, Ali, Gatsis, Nikolaos, Pack, Daniel J., Akopian, David
Format: Magazine Article
Language:English
Published: Los Alamitos IEEE 01.08.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Clustering algorithms
Data analysis
Fingerprinting
Global Positioning System
Indoor positioning
Local area networks
Localization
Mobile computing
Optimization techniques
outlier detection
Outliers (statistics)
Position measurement
Probabilistic logic
Recovery
Sensors
Signal strength
sparse recovery
Wireless LAN
Wireless networks
WLAN fingerprinting
ISSN:1536-1233, 1558-0660
Online Access:Get full text
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Summary:In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location. 2) Access Point (AP) selection and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.
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ISSN:1536-1233
1558-0660
DOI:10.1109/TMC.2016.2616465