Convergence Performance of the Simplified Set-Membership Affine Projection Algorithm

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. Th...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2011-04, Vol.30 (2), p.439-462
Main Author: Diniz, Paulo S. R.
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithms
Circuits and Systems
Complexity
Computation
Convergence
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Mathematical analysis
Power consumption
Projection
Signal,Image and Speech Processing
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Summary: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.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-010-9219-z