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In order to decrease the steady-state error and reduce the computationalcomplexity and increase the ability to identify a large unknown system, aconvex combination of overlap-save frequency-domain adaptive filters (COSFDAF)algorithm is proposed. From the articles available, most papers discuss convexcombinations of adaptive-filter algorithms focusing on the time domain. Thosealgorithms show better performances in convergence speed and steady-stateerror. The major defect of those algorithms, however, is the computationalcomplexity. To deal with this problem and motivated by frequency-domainadaptive filters (FDAF) and convex optimization, this paper gives an adaptivefilter algorithm, that consists of combining the two FDAFs using the convexcombination principles and derives a formula to update the mixing parameter.The computational complexity of the COSFDAF is analyzed theoretically. Thesimulation results show that no matter what kinds of signal to be processed,whether correlated (i.e. colored noise) or uncorrelated (i.e. white noise), theproposed algorithm has better performance in identify the unknown coefficientswhen compared to a single overlap-save FDAF or the convex combination of twotime-domain adaptive filters.

Convex Combination of Overlap-Save Frequency-Domain Adaptive Filters