Title :
Symmetric adaptive predictive structure for tracking channel non-stationarities
Author :
Jebara, S. Ben ; Jaidane-Saidane, M.
Author_Institution :
ENIT, Campus Univ., Tunis, Tunisia
Abstract :
The basic idea of this paper is related to the fact that the steady state property in a non-stationary system context is strongly related to the input correlation characteristics. When the LMS algorithm is used to identify a system with variations modeled by a random walk, the performance is degradated as the input correlation increases. The classical identification scheme can be improved by the use of a prewhitening adaptive filter. A theoretical analysis of an adaptive predictive identification scheme is presented. This study illustrates the contribution of predictive structures for tracking system non-stationarities
Keywords :
adaptive filters; adaptive signal processing; correlation methods; filtering theory; identification; least mean squares methods; prediction theory; random processes; telecommunication channels; tracking; LMS algorithm; adaptive predictive identification; channel nonstationarities tracking; input correlation; nonstationary system; prewhitening adaptive filter; random walk; steady state property; symmetric adaptive predictive structure; system identification; Adaptive algorithm; Additive noise; Algorithm design and analysis; Analytical models; Degradation; Filters; Least squares approximation; Performance analysis; Predictive models; Steady-state;
Conference_Titel :
Digital Signal Processing Workshop Proceedings, 1996., IEEE
Conference_Location :
Loen
Print_ISBN :
0-7803-3629-1
DOI :
10.1109/DSPWS.1996.555531
