MATLAB Implementation of Adaptive Filter with FPGA-Ready Code

This documentation presents a detailed MATLAB implementation of adaptive filters using hardware-optimized model files. The developed models are directly deployable on XILINX FPGAs, offering efficient filtering solutions for real-time signal processing applications. We comprehensively examine adaptive filtering principles including key algorithms like LMS (Least Mean Squares) and RLS (Recursive Least Squares), with MATLAB code demonstrating parameter initialization, weight update mechanisms, and error minimization processes. The implementation includes step-by-step code development for filter coefficients adaptation using iterative algorithms that continuously adjust parameters based on input signal characteristics. The document also covers performance optimization through parameter tuning techniques such as step-size adjustment for convergence control and regularization methods for stability enhancement. Verification methodologies include MATLAB simulations comparing filter outputs with expected results using metrics like Mean Square Error (MSE) and convergence plots. This resource enables engineers to effectively understand and apply adaptive filters in practical engineering projects through executable MATLAB examples and hardware implementation guidelines.