@inproceedings{dcad5e61e05d46f2a4dc670472c07c97,
title = "Fuzzy inference with parameter identification for indoor WLAN positioning",
abstract = "This paper considers the fuzzy inference as position estimator for WLAN indoor environments, based on received signal strength measurements RSS. The proposal algorithm includes a fuzzy inference which uses the k-nearest neighbor classification in signal space, where the position of target node is calculated as a weighted combination of nearest fingerprints, where the weights are estimated using enhanced Takagi-Sugeno fuzzy controller with multivariable inputs and parameter identification with constrained optimization. The new developed technique is proposed to enhance the accuracy of position estimation in WLAN indoor environments.",
keywords = "Clustering, Fuzzy logic, RSS, WLAN indoor positioning",
author = "M. Alakhras and M. Oussalah and Hussein, {M. I.}",
year = "2015",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "641--648",
editor = "Ao, {S. I.} and Len Gelman and Korsunsky, {Alexander M.} and Ao, {S. I.} and Hukins, {David W.L.} and Andrew Hunter and Ao, {S. I.} and Len Gelman",
booktitle = "WCE 2015 - World Congress on Engineering 2015",
note = "2015 World Congress on Engineering, WCE 2015 ; Conference date: 01-07-2015 Through 03-07-2015",
}