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Convex Combination of Overlap-Save Frequency-Domain Adaptive Filters
Systems and Control · Submitted 3 May 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The simulation results show that no matter what kinds of signal to be processed, whether correlated (i.e. colored noise) or uncorrelated (i.e. white noise), the proposed algorithm has better performance in identify the unknown coefficients when compared to a single overlap-save FDAF or the convex combination of two time-domain adaptive filters.
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The abstract, sentence by sentence
In order to decrease the steady-state error and reduce the computational complexity and increase the ability to identify a large unknown system, a convex combination of overlap-save frequency-domain adaptive filters (COSFDAF) algorithm is proposed. From the articles available, most papers discuss convex combinations of adaptive-filter algorithms focusing on the time domain. Those algorithms show better performances in convergence speed and steady-state error. The major defect of those algorithms, however, is the computational complexity. To deal with this problem and motivated by frequency-domain adaptive filters (FDAF) and convex optimization, this paper gives an adaptive filter algorithm, that consists of combining the two FDAFs using the convex combination principles and derives a formula to update the mixing parameter. The computational complexity of the COSFDAF is analyzed theoretically. The simulation results show that no matter what kinds of signal to be processed, whether correlated (i.e. colored noise) or uncorrelated (i.e. white noise), the proposed algorithm has better performance in identify the unknown coefficients when compared to a single overlap-save FDAF or the convex combination of two time-domain adaptive filters.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In order to decrease the steady-state error and reduce the computational complexity and increase the ability to identify a large unknown system, a convex combination of overlap-save frequency-domain adaptive filters (COSFDAF) algorithm is proposed. | contribution 1.00 | 0.92 | 0.28 | asserted 1.00 |
| s1 | From the articles available, most papers discuss convex combinations of adaptive-filter algorithms focusing on the time domain. | background 0.95 | 0.06 | 0.02 | asserted 0.99 |
| s2 | Those algorithms show better performances in convergence speed and steady-state error. | background 1.00 | 0.07 | 0.04 | asserted 1.00 |
| s3 | The major defect of those algorithms, however, is the computational complexity. | background 0.62 | 0.06 | 0.02 | asserted 1.00 |
| s4 | To deal with this problem and motivated by frequency-domain adaptive filters (FDAF) and convex optimization, this paper gives an adaptive filter algorithm, that consists of combining the two FDAFs using the convex combination principles and derives a formula to update the mixing parameter. | contribution 0.59 | 0.95 | 0.10 | asserted 0.99 |
| s5 | The computational complexity of the COSFDAF is analyzed theoretically. | contribution 0.69 | 0.92 | 0.04 | asserted 0.92 |
| s6 | The simulation results show that no matter what kinds of signal to be processed, whether correlated (i.e. colored noise) or uncorrelated (i.e. white noise), the proposed algorithm has better performance in identify the unknown coefficients when compared to a single overlap-save FDAF or the convex combination of two time-domain adaptive filters. | result 1.00 | 0.96 | 0.83 | asserted 1.00 |
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Original abstract
In order to decrease the steady-state error and reduce the computational complexity and increase the ability to identify a large unknown system, a convex combination of overlap-save frequency-domain adaptive filters (COSFDAF) algorithm is proposed. From the articles available, most papers discuss convex combinations of adaptive-filter algorithms focusing on the time domain. Those algorithms show better performances in convergence speed and steady-state error. The major defect of those algorithms, however, is the computational complexity. To deal with this problem and motivated by frequency-domain adaptive filters (FDAF) and convex optimization, this paper gives an adaptive filter algorithm, that consists of combining the two FDAFs using the convex combination principles and derives a formula to update the mixing parameter. The computational complexity of the COSFDAF is analyzed theoretically. The simulation results show that no matter what kinds of signal to be processed, whether correlated (i.e. colored noise) or uncorrelated (i.e. white noise), the proposed algorithm has better performance in identify the unknown coefficients when compared to a single overlap-save FDAF or the convex combination of two time-domain adaptive filters.
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