Statistical learning theory for location fingerprinting in wireless LANs

Abstract

In this paper, techniques and algorithms developed in the framework of Statistical Learning Theory are applied to the problem of determining the location of a wireless device by measuring the signal strength values from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no special-purpose hardware is required.The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in scientific literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques.

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

“…Shin et al [57] presented the EWKNN (Enhanced Weighted K-nearest Neighbor algorithm) algorithm. First, it generated a list of signal strength by comparing fingerprint data in the fingerprint database with signal strength measured while positioning for the first selection.…”

An Indoor Positioning Method using IEEE 802.11 Channel State Information

“…Likelihood (MaxL) [8]. In order to obtain a fair comparison, these schemes use a similarly measured dataset.…”

Unsupervised labelling of sequential data for location identification in indoor environments

“…These differentiated stages are usually referred to as offline and online phases and some examples of this work include Kaemarungsi & Krishnamurthy (2004); Brunato & Battiti (2005); Roos et al (2002); Youssef et al (2003);Yim (2008). Alternative approaches based on triangulation and trilateration (Bahl & Padmanabhan (2000); Krishnan et al (2004); Peterson et al (1998);Li et al (2000)) study physical signal propagations.…”