A robust method for parameter estimation of AR systems using empirical mode decomposition

Abstract

This paper presents a robust algorithm for parameter estimation of autoregressive (AR) systems in noise using empirical mode decomposition (EMD) method. The basic idea is to represent the autocorrelation function of the noise-free AR signal as the summation of damped sinusoidal functions and use EMD for extracting these component functions as intrinsic mode functions (IMFs). Unlike conventional correlation-based techniques, the proposed scheme first estimates the damped sinusoidal model parameters from the IMFs of autocorrelation function using a least-squares based method. The AR parameters are then directly obtained from the extracted sinusoidal model parameters. Simulation results show that EMD is a very promising tool for AR system identification at a very low signal-to-noise ratio (SNR).