论文信息 - Comparison of based adaptive predictive schemes for improvement of tracking randomly time-varying systems

Comparison of based adaptive predictive schemes for improvement of tracking randomly time-varying systems

This paper presents a comparison between three based adaptive predictive schemes used in order to improve the tracking capability of the LMS algorithm. We identify system variations modeled by a random walk. Using a theoretical analysis and simulation results, we illustrate the contribution of coupled adaptive prediction and system identification for highly correlated stationary inputs and nonstationary (speech) inputs.

M. Jaidane-Saidane | S.B. Jebaral

[1] M. Jaidane-Saidane,et al. Symmetric adaptive predictive structure for tracking channel non-stationarities , 1996, 1996 IEEE Digital Signal Processing Workshop Proceedings.

[2] Joe Brewer,et al. Kronecker products and matrix calculus in system theory , 1978 .

[3] Eweda Eweda,et al. Comparison of RLS, LMS, and sign algorithms for tracking randomly time-varying channels , 1994, IEEE Trans. Signal Process..

[4] Mamadou Mboup,et al. LMS coupled adaptive prediction and system identification: a statistical model and transient mean analysis , 1994, IEEE Trans. Signal Process..

[5] Sofia Ben Jebara,et al. Best input for optimal tracking randomly time-varying systems: justification of adaptive predictive structure , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.