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Author Nakib, A. ♦ Daachi, B. ♦ Dakkak, M. ♦ Siarry, P.
Sponsorship IEEE Computer Society
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2002
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Trajectory ♦ Target tracking ♦ Kalman filters ♦ Noise measurement ♦ Correlation ♦ Mobile computing ♦ Mobile communication ♦ Computing Methodologies ♦ Mathematics of Computing ♦ General ♦ Numerical Analysis ♦ Approximation ♦ Linear approximation ♦ Wavelets and fractals ♦ Applications ♦ Roots of Nonlinear Equations ♦ Iterative methods ♦ Mobile environments ♦ Mobile Computing ♦ Communication/Networking and Information Technology ♦ Computer Syst ♦ Algorithm/protocol design and analysis ♦ Signal processing systems ♦ Special-Purpose and Application-Based Systems ♦ Computer Systems Organization ♦ Probability and Statistics ♦ Time series analysis ♦ Robust regression ♦ Correlation and regression analysis ♦ Kalman filter ♦ Indoor location ♦ path tracking ♦ digital fractional integration ♦ prediction filters ♦ mobile location
Abstract While the static indoor geo-location of mobile terminals (MTs) has been extensively studied in the last decade, the prediction of the trajectory of an MT is still a major problem when designing mobile location (tracking) systems (TSs). In fact, Global Positioning System (GPS) works quite well in outdoor conditions and relatively unobstructed spaces, but falls short in many urban conditions and other realistic use cases. It is important to augment mobile geo-location architectures with a prediction dimension to deal with distortions caused by obstacles, and ultimately produce a more accurate positioning system. Different prediction approaches have been proposed in the literature, the most common is based on prediction filters such as linear predictors (LPs), Kalman filters (KFs), and particle filters (PFs). In this paper, we take the prediction one step further by using digital fractional integration (DFI) to predict the actual trajectory of MTs. We evaluate the performance of our proposed DFI prediction in two indoor trajectory scenarios inspired by typical user mobility patterns in typical indoor conditions (museum visit and hospital doctor walk). To illustrate the efficiency of the proposed method in particularly noisy environments, we consider two other MT trajectory scenarios, namely spiral and sinusoidal trajectories. Experimental results show a significant performance improvement over most common predictors in the relevant literature, particularly in noisy cases. Extensive study of short-archive principle using 5, 10, and 25 previous estimated positions, showed the benefit of using DFI operator with only the most recent locations of an MT.
Description Author affiliation :: Lab. Images, Signaux et Syst. Intelligents, Univ. de Paris-Est Creteil, Créteil, France
ISSN 15361233
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-01-01
Publisher Place U.S.A.
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Volume Number 13
Issue Number 10
Size (in Bytes) 2.74 MB
Page Count 14
Starting Page 2306
Ending Page 2319

Source: IEEE Xplore Digital Library