TY - JOUR ID - 441 TI - Indoor Positioning and Pre-processing of RSS Measurements JO - AUT Journal of Electrical Engineering JA - EEJ LA - en SN - 2588-2910 AU - Pourahmadi, V. AU - Valaee, S. AD - Assistant Professor, Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran AD - Professor, ECE department, University of Toronto, Toronto, Canada Y1 - 2014 PY - 2014 VL - 46 IS - 1 SP - 59 EP - 66 KW - Recovered Hidden Markov Model KW - Electric Arc Furnace KW - Voltage Flicker KW - Power Quality parameters DO - 10.22060/eej.2014.441 N2 - Rapid expansions of new location-based services signify the need for finding accurate localization techniques for indoor environments. Among different techniques, RSS-based  schemes and in particular oneof its variants which is based on Graph-based Semi-Supervised Learning (G-SSL) are widely-used approaches The superiority of this scheme is that it has low setup/training cost and at the same time it leads to low localization error. Analyzing the G-SSL method we can observe that its performance is highly dependent on its inputs (RSS measurements). The main objective of this work is to further improve the accuracy of G -SSL based schemes by performing multiple RSS measurements and then passing them through pre-processing blocks to improve the reliability of the corresponding RSS vector at each Sample Points (SPs). Experimental results are then followed to show the performance of the proposed method compared to what we get with the original G-SSL approach. UR - https://eej.aut.ac.ir/article_441.html L1 - https://eej.aut.ac.ir/article_441_d04b08942bfa3c43c3df1ccf086deaf5.pdf ER -