Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30075
An Estimation of the Performance of HRLS Algorithm

Authors: Shazia Javed, Noor Atinah Ahmad

Abstract:

The householder RLS (HRLS) algorithm is an O(N2) algorithm which recursively updates an arbitrary square-root of the input data correlation matrix and naturally provides the LS weight vector. A data dependent householder matrix is applied for such an update. In this paper a recursive estimate of the eigenvalue spread and misalignment of the algorithm is presented at a very low computational cost. Misalignment is found to be highly sensitive to the eigenvalue spread of input signals, output noise of the system and exponential window. Simulation results show noticeable degradation in the misalignment by increase in eigenvalue spread as well as system-s output noise, while exponential window was kept constant.

Keywords: HRLS algorithm, eigenvalue spread, misalignment.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078062

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1226

References:


[1] A. H. Sayed and T. Kailath, A state-space approach to adaptive RLS filtering, IEEE Signal Processing Magazine,1994.
[2] Jr. Apolinario, QRD-RLS Adaptive filtering, Springer, 2009.
[3] S. C. Douglas, Numerically robust O(N2) RLS algorithms using Least Squares Prewhitening, IEEE,2000.
[4] J. Benesty and T. Gansler, A recursive estimation of the condition number in the RLS algorithm, IEEE,2005.
[5] F. Beaufays, Transform-Domain Adaptive Filters: An Analytical Approach, IEEE transactions on signal processing, 1995.
[6] N. A. Ahmad, Comparative study of iterative search method for adaptive filtering problems, International Conference on Applied mathematics, 2005.
[7] B. Farhang-Boroujeny, Adaptive filters: theory and applications, John Wiley & Sons, Inc.,1998.
[8] S. Haykin, Adaptive Filter Theory, 2nd edition, Prentice Hall,1991.