On length adaptation for the least mean square adaptive filters
Signal Processing - Fractional calculus applications in signals and systems
An improved variable tap-length LMS algorithm
Signal Processing
Adaptive filter length selection for acoustic echo cancellation
Signal Processing
Energy Aware Algorithm and Implementation of SDR Oriented HSDPA Chip Level Equalizer
Journal of Signal Processing Systems
The quaternion LMS algorithm for adaptive filtering of hypercomplex processes
IEEE Transactions on Signal Processing
Adaptive nonlinear system identification in the short-time fourier transform domain
IEEE Transactions on Signal Processing
A VLMS based pseudo-fractional optimum order estimation algorithm
Proceedings of the 2011 International Conference on Communication, Computing & Security
International Journal of Computational Vision and Robotics
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Searching for the optimum tap-length that best balances the complexity and steady-state performance of an adaptive filter has attracted attention recently. Among existing algorithms that can be found in the literature, two of which, namely the segmented filter (SF) and gradient descent (GD) algorithms, are of particular interest as they can search for the optimum tap-length quickly. In this paper, at first, we carefully compare the SF and GD algorithms and show that the two algorithms are equivalent in performance under some constraints, but each has advantages/disadvantages relative to the other. Then, we propose an improved variable tap-length algorithm using the concept of the pseudo fractional tap-length (FT). Updating the tap-length with instantaneous errors in a style similar to that used in the stochastic gradient [or least mean squares (LMS)] algorithm, the proposed FT algorithm not only retains the advantages from both the SF and the GD algorithms but also has significantly less complexity than existing algorithms. Both performance analysis and numerical simulations are given to verify the new proposed algorithm.