Partial update EDS algorithms for adaptive filtering

In practice, computational complexity is an important consideration of an adaptive signal processing system. A well-known approach to controlling computational complexity is applying partial update (PU) adaptive filters. In this paper, a partial update Euclidean Direction Search (EDS) algorithm is employed. The theoretical analyses of mean and mean-square performance are presented. The simulation results of different PU EDS are shown.

[1]  Li Xiao,et al.  Performance Analysis of the Deficient Length EDS Adaptive Algorithm , 2006, APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems.

[2]  Paulo Sergio Ramirez,et al.  Fundamentals of Adaptive Filtering , 2002 .

[3]  S. S. Riaz Ahamed PERFORMANCE ANALYSIS OF DWDM , 2008 .

[4]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[5]  T. Bose,et al.  Performance Analysis of Deficient-length RLS and EDS Algorithms , 2009, 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop.

[6]  Khaled A. Mayyas,et al.  Performance analysis of the deficient length LMS adaptive algorithm , 2005, IEEE Transactions on Signal Processing.

[7]  Tamal Bose,et al.  Digital Signal and Image Processing , 2003 .