  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.
