Talks and Poster Presentations (with Proceedings-Entry):
G. Retscher, J. Joksch:
"Analysis of Nine Vector Distances for Fingerprinting in Multiple-SSID Wi-Fi Networks";
Poster: 7th International Conference Indoor Positioning and Indoor Navigation IPIN 2016,
Alcalá de Henares (invited);
- 2016-10-06; in: "7th International Conference Indoor Positioning and Indoor Navigation IPIN 2016",
Paper ID 190,
In this analytical study, the use of nine different vector distances, i.e., the Manhattan, Euclidean, Chebyshev, Canberra, Cosine, Sorensen, Hellinger, Chi-square and Jeffrey distance, for positioning using Wi-Fi location fingerprinting is evaluated. A testbed in an office environment with a regular grid of reference points was built therefore. RSS measurements to six access points of four different multiple-SSID (Service Set Identifier) Wi-Fi networks are compared. The achievable positioning accuracies depended mainly on the selection of the vector distance and matching algorithm, i.e., the nearest neighbour NN, k-nearest KNN or k-weighted nearest neighbour KWNN approach. In most tests a similar performance was achieved with the Cosine, Euclidean, Hellinger and Chi-square vector distance in combination with the KNN or KWNN. In average the mean distance error (MDE) resulted in 1.4 m if using a single network in multiple-SSID configuration.
Wi-Fi positioning; multiple-SSID networks; location fingerprinting; matching approaches; vector distances; mean distance error
Created from the Publication Database of the Vienna University of Technology.