Adaptive Filter Design using LMS algorithm
This project demonstrates the design and performance analysis of an adaptive filter using the Least Mean Squares (LMS) algorithm, and compares its performance with static FIR filters designed using standard window functions (like Hamming, Hanning, and Blackman). The objective is to showcase how adaptive filtering adjusts to varying signal environments, unlike fixed-coefficient filters.
- Implement LMS-based adaptive filtering for noise reduction.
- Design FIR filters using static window techniques (Hamming, Hanning, etc.).
- Compare performance in terms of convergence rate, error minimization, and adaptability to non-stationary signals.
- Visualize and analyze filter outputs in MATLAB.