Basis Expansion Adaptive Filters for Time-Varying System Identification

In this paper, we extend the concept of block adaptive filters to what we call basis expansion adaptive filters. While in block adaptive filters the system is assumed to be constant within a block, our basis expansion adaptive filters model the time variation of the system within a block by a set of basis functions. This allows us to improve the tracking performance of block adaptive filters considerably. We focus on stochastic gradient type of adaptive filters, although extensions to other types of adaptive filters can be envisioned.