DocumentCode :
2168790
Title :
Low-order Kalman filters for channel estimation
Author :
McGuire, Michael ; Sima, Mihai
Author_Institution :
Dept. of Electr. & Comput. Eng., Victoria Univ., BC, Canada
fYear :
2005
fDate :
24-26 Aug. 2005
Firstpage :
352
Lastpage :
355
Abstract :
This paper addresses the design of low-order Kalman filters to estimate radio channels with Rayleigh fading. Rayleigh fading cannot be perfectly modelled with any finite order auto-regressive (AR) process. Previously, only first and second order Kalman filters were used for channel estimation since higher order Kalman filters were found to not significantly improve accuracy. This is due to mismatches in the statistics of the AR models of the Kalman filters and the true Rayleigh fading. In this paper, the coefficients of the AR models for the Kalman filter are calculated by solving for the minimum square error solutions of an over-determined linear systems. The AR models generated have statistics closely matching the Rayleigh fading process. The Kalman filter using these AR models can accurately estimate the Rayleigh fading process. The accuracy of the new Kalman filters is demonstrated in the tracking of simulated Rayleigh fading processes of different bandwidths.
Keywords :
Kalman filters; Rayleigh channels; autoregressive processes; channel estimation; mean square error methods; wireless channels; Rayleigh fading process; auto-regressive process; channel estimation; estimate radio channels; low-order Kalman filters; minimum square error solutions; over-determined linear systems; Channel estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, Computers and signal Processing, 2005. PACRIM. 2005 IEEE Pacific Rim Conference on
Print_ISBN :
0-7803-9195-0
Type :
conf
DOI :
10.1109/PACRIM.2005.1517298
Filename :
1517298
Link To Document :
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