Abstract—A state space model for mobile terminal motion is
presented which has the properties observed in true terminal
motion. This model is used with a Kalman filter to combine the
information of location estimates made at different times into
an improved location estimate. This paper also provides
experimental The performance comparison of the conventional
non linear Kalman Filters and their adaptive variants for
mobile dynamic location in urban area.The methodology uses
TEMS Investigation software to retrieve network information
including signal strength and cell-identities of various base
transmitter stations (BTS). The distance from the mobile station
(MS) to each BTS is therefore determined using
Walfish-Ikigami radio propagation model. The different
distances are therefore combined in the framework of nonlinear
Kalman filter variants. In this work we compare the
performance of four algorithms, based on the nonlinear
Kalman Filter. For the mobile terminal localization, the results
show that both of EKF, AEKF, UKF and AUKF work
comparably well, in spite of the superior performance of the
UKF and AUKF algorithms.
Index Terms—Mobile localization, nonlinear kalman filter
variants, noise covariance adaption, cellular network.
N. Bouzera, N. Mezhoud, and A. Khireddine are with the Genie Electric
Laboratory (LGEB), Faculty of Technology, University of Bejaia,
TargaOuzamour, 06000 Bejaia, Algeria (e-mail: n.bouzera@gmail.com,
mezhoud.naima@gmail.com, abdelkrim.khired@gmail.com).
M. Oussalah is with the University of Birmingham, Electronics,
Electrical and Computer Engineering Edgbaston, Birmingham B15 2TT
(e-mail: m.oussalah@bham.ac.uk).
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Cite:N. Bouzera, N. Mezhoud, A. Khireddine, and M. Oussalah, "Performance Comparison of the Kalman Filter Variants for Dynamic Mobile Localization in Urban Area Using Cellular Network," International Journal of Computer Theory and Engineering vol. 7, no. 5, pp. 354-361, 2015.
