AR Method-Based Spectral Estimation Algorithm

In this article, we explore a spectral estimation algorithm based on the autoregressive (AR) method. This algorithm is widely used in signal processing applications, particularly in communication systems. Its exceptional performance makes it one of the preferred methods for estimating signal power spectral density. We provide a MATLAB implementation that demonstrates key computational steps including the Yule-Walker equations for parameter estimation and the use of the aryule function for autoregressive model coefficients calculation. The implementation includes waveform generation, parameter optimization using techniques like the Akaike Information Criterion (AIC) for model order selection, and spectral density computation through the freqz function for frequency response analysis. Through MATLAB's visualization capabilities, users can intuitively observe the algorithm's performance through power spectrum plots and spectral comparison graphs, gaining deeper insights into its working principles and advantages in resolution enhancement compared to classical periodogram methods.