Paper Info
Title
Clipped LMS/RLS Adaptive Algorithms: Analytical Evaluation and Performance Comparison with Low-Complexity Counterparts
Abstract
The high computational load of conventional adaptive FIR filters applied to long system identification and their weak tracking ability has encouraged researchers to seek for efficient adaptive algorithms for this kind of applications. One of the efficient solutions is the three-level clipped LMS/RLS adaptive algorithm. In this paper, an insight into the performance of these adaptive algorithms that evaluates the amount of steady-state misalignment error of clipped LMS/RLS adaptive algorithms employed for identification of time-invariant and time-varying systems, is presented. Employing it, we compare the misalignment performance with their low-complexity adaptive algorithm counterparts theoretically. In addition, we derive the optimal step size/forgetting factor explicitly and also obtain a relation between the optimal level of the clipping and step size/forgetting factor to achieve the lowest steady-state misalignment and then we explain as to how to improve the performance of these kinds of adaptive algorithms by adjusting the clipping threshold according to the noise level in such a way that a higher performance is achieved. Finally, different adaptive algorithms have been further coded in VHDL in order to evaluate them in terms of speed and hardware resources.
Year
DOI
Venue
2015
10.1007/s00034-014-9923-1
Circuits, Systems, and Signal Processing
Keywords
Field
DocType
Adaptive filter, Convergence rate, Misalignment, Clipping, System identification
Control theory,Computer science,Noise level,Algorithm,Adaptive filter,Rate of convergence,VHDL,Adaptive algorithm,Finite impulse response,System identification,Clipping (audio)
Journal
Volume
Issue
ISSN
34
5
1531-5878
Citations 
PageRank 
References 
0
0.34
9
Authors
2
Name
Order
Citations
PageRank
Mehdi Bekrani142.82
Mojtaba Lotfizad2489.66