Principles of Adaptive Filters with MATLAB Implementation

This article explores the fundamental principles of adaptive filters and provides a practical MATLAB implementation example. Adaptive filters are dynamic digital filters capable of automatically adjusting their filtering parameters based on input signal characteristics. These filters find extensive applications in signal processing, communication systems, audio processing, and various engineering domains. The MATLAB implementation includes core algorithms such as Least Mean Squares (LMS) or Recursive Least Squares (RLS), demonstrating parameter adaptation through weight update equations. Key MATLAB functions like filter(), adaptfilt.lms, and custom adaptation loops illustrate real-time coefficient adjustments. Through this programmable approach, users can modify filter parameters (step size, filter order) and observe convergence behavior, enabling deeper understanding of adaptive filtering applications in noise cancellation, system identification, and channel equalization scenarios.