Abstract
Adaptive filtering is an important subfield of digital signal processing having been actively researched for more than four decades and having important applications such as noise cancellation, system identification, and telecommunications channel equalization. In this paper we provide a novel framework for adaptive filtering based on the theory stationary iterative linear equation solvers. We show that a large number of established, and some quite novel, adaptive filtering algorithms can be interpreted as special cases of a generic update equation forming the cornerstone of our framework. The novelty of our contribution is the casting of the adaptive filtering problem as a problem in numerical linear algebra facilitating a clearer understanding and a unified analysis of the various algorithms.KeywordsDigital Signal ProcessingLittle Mean SquareAdaptive FilterRecursive Little SquareNoise CancellationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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