Common Methods for Power Spectrum Estimation

The article discusses several common methods for power spectrum estimation, primarily including the Periodogram method, Welch's method, and AR model approach. These techniques serve as essential tools for analyzing signal spectral characteristics and are widely applied in signal processing fields. The Periodogram method estimates power spectrum by computing signal periodicity, typically implemented using Fast Fourier Transform (FFT) algorithms. Welch's method improves spectral estimation by dividing signals into multiple overlapping segments and averaging their periodograms, requiring parameters like segment length and overlap percentage. The AR model approach utilizes autoregressive modeling to predict signal spectrum distribution, where model order selection (e.g., using Akaike Information Criterion) becomes crucial for accurate estimation. Through these methods, we can better understand signal spectral properties, enabling more effective analysis and processing of signal data with considerations for computational efficiency and spectral resolution trade-offs.