Simulation of Wiener-Hopf Adaptive Algorithm for Narrowband Signal Processing

This paper presents a simulation of the Wiener-Hopf adaptive algorithm. The algorithm is suitable for narrowband signal processing and operates effectively under Gaussian white noise conditions. The Wiener-Hopf adaptive algorithm represents a classical adaptive filtering approach that automatically adjusts filter parameters based on statistical characteristics of input signals, thereby achieving optimal signal filtering. In this simulation, we investigate the performance characteristics of the Wiener-Hopf adaptive algorithm and conduct comprehensive analysis and evaluation. The implementation utilizes MATLAB's signal processing toolbox functions such as wiener for optimal filter design and includes custom code for adaptive parameter updates. We demonstrate how to perform Wiener-Hopf adaptive algorithm simulations using MATLAB software, providing complete program code and simulation results. Through mathematical formulations of the mean-square error minimization principle and code examples showing real-time coefficient adaptation, readers will gain deep understanding of the algorithm's theoretical foundations and practical applications. The article enables researchers to master MATLAB-based simulation techniques for effectively implementing this algorithm in practical engineering scenarios, with particular emphasis on convergence behavior analysis and noise rejection performance metrics.