Power Spectrum Estimation of Turbulent Meteorological Signals Based on the autoregressive Model

This paper explores various spectral estimation methods used in Doppler radar signal processing, specifically for turbulent meteorological signals. The study compares the traditional periodogram method, Pulse Pair Processing (PPP) method, and two autoregressive (AR) models: Yule-Walker and Burg methods. These methods are evaluated based on their ability to estimate Doppler velocity and spectral width under different signal-to-noise ratios (SNR) and sampling points. Simulation results show that the AR-Yule-Walker method provides the most accurate and robust performance, especially under low SNR conditions. The Burg method, while effective, shows higher estimation errors in spectral width compared to Yule-Walker. This research highlights the importance of selecting appropriate estimation methods to enhance the accuracy of meteorological observations, particularly when data availability is limited or under noisy conditions. The findings contribute to improving Doppler radar applications in meteorology, such as wind field measurement and weather forecasting.