Fast Algorithms for Fixed-Order Recursive Least-Squares Parameter Estimation.

摘要

Recursive Least-Squares (RLS) algorithms are a family of widely-used techniques for adaptive parameter estimation and filtering. In many applications, a special structure in the estimation problem can be exhibited. This structure can be exploited to arrive at fast RLS algorithms. In this dissertation, we focus mainly on fast algorithms based on certain shift-invariance properties, and the particular filter structure considered will be a so-called tapped delay-line or transversal filter structure. Single-channel applications include high resolution spectrum estimation (AR modeling), noise cancellation, speech and biomedical signal processing. The multichannel algorithms (where each channel feeds a tapped delay-line) accommodate such applications as identification of systems described by difference equations with multiple polynomials (e.g. ARX and ARMAX models), adaptive minimum-variance control, fractionally-spaced and decision-feedback equalizers, multirate signal processing, image enhancement, and adaptive broadband beamforming.