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Set-Membership Constrained Frequency-Domain Algorithm
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DOI：10.1109/TCSII.2020.3017003.png)
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Abstract
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In this brief, we propose a new frequency-domain adaptive filtering algorithm within the set-membership filtering (SMF) paradigm. Algorithms employing the previously proposed frequency-domain SMF (FD-SMF) concept are not capable of performing linear convolutions (i.e., linear filtering operations), but only circular convolutions. Here, we overcome such limitation by generalizing the FD-SMF concept to allow for linear convolutions, and we propose a new algorithm following this extended FD-SMF concept. Synthetic and real-life simulation results confirm that the proposed algorithm can perform quite better than its non-SMF counterpart, achieving faster convergence and requiring much fewer computations.
Keywords:
Frequency-domain analysis
Convolution
Signal processing algorithms
Discrete Fourier transforms
Circuits and systems
Convergence
Standards
Adaptive filtering
data-selection
set-membership filtering
frequency-domain
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