Hybrid algorithm for indoor positioning using wireless LAN
Kwon, J.
Dundar, B.
Varaiya, P.
Inst. of Transp. Studies, California Univ., Berkeley, CA, USA;
This paper appears in: Vehicular Technology Conference, 2004. VTC2004-Fall. 2004 IEEE 60th
Publication Date: 26-29 Sept. 2004
Volume: 7,
On page(s): 4625- 4629 Vol. 7
ISSN: 1090-3038
ISBN: 0-7803-8521-7
INSPEC Accession Number: 8456342
Digital Object Identifier: 10.1109/VETECF.2004.1404967
Current Version Published: 2005-04-25
Abstract
Locating an indoor mobile station based on a wireless communication infrastructure, has practical applications. The most widely employed methods today use an RF propagation loss (PL) model or location fingerprinting (LF). The PL method is known to perform poorly compared to LF. But LF requires an extensive training dataset and cannot adapt well to configuration changes or receiver breakdown. In this paper, we develop a hybrid method that combines the strength of these two methods. It first formulates the RF PL in a nonlinear, censored regression model and adjusts the regression function to the observed signal strength in the fingerprint dataset. In the absence of a training dataset, the hybrid method coincides with the PL method, and, as the spatial granularity of the training dataset increases, the result of the algorithm approaches the result of the LF method. It balances the flexibility and accuracy of the two traditional methods, makes intelligent use of missing values, produces error bounds, and can be made dynamic. We evaluate the performance of the algorithm by applying it to a real site and observe satisfactory positioning accuracy.
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