A low-cost implementation strategy for combinations of adaptive filters

Combinations of adaptive filters have attracted attention as a simple way to improve filter performance, avoiding the compromise between fast adaptation and low excess mean-square error. However, the computational cost of the combined scheme is higher than that of a single filter, since two or more filters must be run in parallel. In this paper, we propose a new approach for reducing the computational cost of combinations of adaptive filters in custom and semi-custom hardware implementations. In the proposed scheme, the fast filter is adapted as usual, but instead of updating the slow filter, we adapt the difference between the slow and fast filters. We show that the coefficients of the difference filter have a lower dynamic range compared to the slow filter, and therefore can be updated using a smaller number of bits. The wordlength used for the difference filter can be chosen to make the performance of this low-cost scheme similar to that of an infinite-precision implementation.