LMS Adaptive Filtering Algorithm - A Comprehensive Implementation Guide

The LMS adaptive filtering algorithm serves as a fundamental filtering technique extensively applied in signal processing and communication systems. This comprehensive code collection implements several commonly used LMS algorithm variants to facilitate better understanding and practical application. The implementations include: - Basic LMS Algorithm: Features gradient descent optimization with weight update mechanism using instantaneous error measurements - Decorrelation LMS Algorithm: Incorporates decorrelation techniques to improve convergence speed in correlated signal environments - Filtered-x LMS Algorithm: Specifically designed for active noise control applications with secondary path compensation - Transform-domain LMS Algorithm: Utilizes frequency-domain processing for enhanced computational efficiency Each algorithm demonstrates unique advantages across different application domains, providing engineers and researchers with versatile tools for adaptive filtering challenges. The code includes parameter tuning guidelines, convergence analysis, and real-time implementation considerations to support both academic research and industrial applications.