This document provides an overview of adaptive filtering techniques. It discusses digital filters and classifications such as linear/nonlinear and finite impulse response (FIR)/infinite impulse response (IIR). It then covers Wiener filters, including how they minimize mean square error. The method of steepest descent is presented as an approach to solve the Wiener-Hopf equations to find optimal filter weights. Finally, it discusses how the least mean squares (LMS) algorithm can be used for adaptive filtering by updating filter weights recursively in the direction that reduces mean square error.
This document provides an overview of adaptive filtering techniques. It discusses digital filters and classifications such as linear/nonlinear and finite impulse response (FIR)/infinite impulse response (IIR). It then covers Wiener filters, including how they minimize mean square error. The method of steepest descent is presented as an approach to solve the Wiener-Hopf equations to find optimal filter weights. Finally, it discusses how the least mean squares (LMS) algorithm can be used for adaptive filtering by updating filter weights recursively in the direction that reduces mean square error.