Skip to content

References: Adaptive Signal Processing

  1. Adaptive filter - Wikipedia - Introduces adaptive filter structure, error-driven coefficient updates, system identification, prediction, equalization, and noise-cancellation applications.

  2. Least mean squares filter - Wikipedia - Derives LMS updates, convergence behavior, stability bounds, normalized variants, and computational requirements.

  3. Recursive least squares filter - Wikipedia - Explains exponentially weighted least squares, recursive updates, forgetting factors, convergence advantages, and higher computational cost.

  4. Adaptive Filter Theory (5th Edition) - Simon Haykin - Pearson - Haykin’s geometric error-surface interpretation and unified progression from Wiener theory to LMS and RLS shaped adaptive-filter pedagogy.

  5. Adaptive Signal Processing - Bernard Widrow and Samuel D. Stearns - Prentice Hall - Widrow’s noise-canceling diagrams and ADALINE examples established intuitive application-driven explanations of adaptation and learning.

  6. SciPy Signal Processing - SciPy - Supplies filtering, correlation, spectral estimation, and system-analysis primitives needed to implement and evaluate adaptive algorithms.

  7. An Introduction to Statistical Signal Processing - Robert M. Gray and Lee D. Davisson - Free text develops estimation, random processes, correlation, linear prediction, and Wiener filtering foundations.

  8. EE 278 Probability and Statistical Inference Notes - Stanford University - Covers MMSE estimation, innovations, Wiener filtering, spectral factorization, linear prediction, and random-process inputs.

  9. Signals and Systems - MIT OpenCourseWare - Reinforces convolution, correlation, frequency response, and feedback concepts that adaptive filters continually estimate or modify.

  10. The Scientist and Engineer's Guide to DSP - Steven W. Smith - Offers practical background on optimal filtering, noise reduction, correlation, and implementation needed before adaptive methods.