A fast on-line algorithm for computing reduced-rank Wiener filters

The reduced-rank Wiener filter (RRWF) can be utilized wherever a desired signal needs to be extracted from random background noise or deterministic interference. Common applications are echo cancellation, equalization, neural network learning and spectral line enhancement. This paper introduces a novel algorithm for the fast online computation of the RRWF. The algorithm is derived by making a certain approximation to the alternating power (AP) method to reduce its computational complexity from O(m²r) to O[max(m²,mn)], or O(mn) if the input is white. Simulations show that, somewhat surprisingly, the computational saving does not come at the cost of estimation accuracy or convergence speed