Paper Info
Title
An iterative scheme for feature based positioning using weighted dissimilarity measure.
Abstract
We propose an iterative scheme for feature-based positioning using the weighted dissimilarity measure with the goal of reducing large errors. The computation of the weights is derived from the robustly estimated variability fo the reference fingerprint map (RFM). The location-wise standard deviation estimation for each individual feature, which is treated as an additional layer of the RFM, is obtained by analyzing the kinematically collected RFM using spatial filtering and kernel smoothing. during the positioning stage, the weighting scheme iteratively adapts the contribution of each feature to the dissimilarity measure, which quantifies the difference between the online measured features and the ones stored in the RFM, according to its variability when searching the candidate estimation of the user's position. These searched locations are subsequently used for refining the estimation of the user's location.
Year
Venue
DocType
2019
CoRR
Journal
Volume
Citations 
PageRank 
abs/1905.08022
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
caifa1224.13
Andreas Wieser2387.88