Set-Membership Constrained Frequency-Domain Algorithm

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.