2-DIMENSIONAL LMS ADAPTATION STRATEGIES FOR NONSTATIONARY SIGNALS

In this paper we propose a new two-dimensional least mean squares algo rithm (2D-LMS) which is able to track nonstationarities in both vertic al and horizontal directions with a computational load comparable to 1 D-LMS methods of the same number of weights. The main difference of ou r method consists in the proposed strategy to run the image in order t o update the filter weights. Smaller initial transients, as well as a reduction in computational load and storage are achieved. Simulations comparing the behavior of our method to recently published methods of 2D-LMS adaptive filtering, have been carried out, showing the main adv antages of the proposed method. (C) 1997 Academic Press.