Separate-variable adaptive combination of LMS adaptive filters for plant identification

Abstract

The Least Mean Square (LMS) algorithm has become a very popular algorithm for adaptive filtering due to its robustness and simplicity. An adaptive convex combination of one fast a one slow LMS filters has been previously proposed for plant identification, as a way to break the speed vs precision compromise inherent to LMS filters. In this paper, an improved version of this combination method is presented. Instead of using a global mixing parameter, the new algorithm uses a different combination parameter for each weight of the adaptive filter, what gives some advantage when identifying varying plants where some of the coefficients remain unaltered, or when the input process is colored. Some simulation examples show the validity of this approach when compared with the one-parameter combination scheme and with a different multi-step approach.