Abstract Over the past few years a number of new adaptive filter algorithms have been developed and applied to meet demands for faster convergence and better tracking properties than earlier techniques could offer. Applications include adaptive channel equalization, adaptive predictive speech coding and on-line system identification. This paper surveys sequential filter adaptation techniques and some applications for transversal FIR, lattice and recursive filters. The available techniques fit into two main categories: (1) gradient-type methods (exemplified by the well-known LMS algorithm) in which successive corrections to adaptive system parameters are only correct in an average sense, and (2) recursive least-squares methods, which continuously provide the solution to a numerical optimization problem, given all the preceding data. The available techniques span a wide spectrum of possible performance/complexity tradeoffs.
Adaptive filter theory and applications
1980-01-01
28 pages
Article/Chapter (Book)
Electronic Resource
English
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