Title :
A comparative study of some simplified RLS-type algorithms
Author :
Husoy, J.H. ; Abadi, Mohammad Shams Esfand
Author_Institution :
Stavanger Univ. Coll., Norway
Abstract :
The recursive least squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Rather than focusing on full RLS algorithm implementations aiming directly at remedying these problems, we argue that the use of simplified or partial RLS algorithms may be a viable alternative to full RLS. In particular, we point out that two recently introduced algorithms, fast Euclidian direction search (FEDS) and recursive adaptive matching pursuit (RAMP) can indeed be interpreted as such partial RLS algorithms exhibiting a nice tradeoff between complexity and performance. We support our presentation by a comprehensive set of simulation results.
Keywords :
adaptive filters; computational complexity; convergence of numerical methods; filtering theory; least squares approximations; RLS algorithm; adaptive filtering algorithm; computational complexity; fast Euclidian direction search; recursive adaptive matching pursuit; recursive least squares; Adaptive filters; Autocorrelation; Computational complexity; Convergence; Educational institutions; Filtering algorithms; Least squares approximation; Matching pursuit algorithms; Pursuit algorithms; Resonance light scattering;
Conference_Titel :
Control, Communications and Signal Processing, 2004. First International Symposium on
Print_ISBN :
0-7803-8379-6
DOI :
10.1109/ISCCSP.2004.1296509