Paper Info
Title
Convergence Performance of the Simplified Set-Membership Affine Projection Algorithm
Abstract
Set-membership (SM) adaptive filtering is appealing in many practical situations, particularly those with inherent power and computational constraints. The main feature of the SM algorithms is their data-selective coefficient update leading to lower computational complexity and power consumption. The set-membership affine projection (SM-AP) algorithm does not trade convergence speed with misadjustment and computation complexity as many existing adaptive filtering algorithms. In this work analytical results related to the SM-AP algorithm are presented for the first time, providing tools to setup its parameters as well as some interpretation to its desirable features. The analysis results in expressions for the excess mean square error (MSE) in stationary environments and the transient behavior of the learning curves. Simulation results confirm the accuracy of the analysis and the good features of the SM-AP algorithms.
Year
DOI
Venue
2011
10.1007/s00034-010-9219-z
CSSP
Keywords
Field
DocType
Adaptive filter analysis, Analysis of the set-membership affine projection algorithm
Convergence (routing),Affine shape adaptation,Mathematical optimization,Harris affine region detector,Expression (mathematics),Affine projection algorithm,Adaptive filter,Learning curve,Mathematics,Computational complexity theory
Journal
Volume
Issue
ISSN
30
2
1531-5878
Citations 
PageRank 
References 
7
0.54
15
Authors
1
Name
Order
Citations
PageRank
Paulo S. R. Diniz124738.72