Parameter Estimation of Fractional Low Order Time-frequency Autoregressive Based on Infinite Variance Analysis

Abstract

Abstract:The Parameter model analysis algorithms include autoregressive (AR) model, moving average (MA) and autoregressive moving average (ARMA) model The existing TFAR model is improved, the new fractional low order timefrequency autoregressive(FLO-TFAR) model and the concept of generalized TF-Yule-Walker equation are proposed, fractional low-order covariance is instead of autocorrelation in the model, The parameter estimation of the model is derived, spectrum estimation algorithm based on the FLO-TFAR model is presented, and the steps of the algorithm are summarized. The detailed comparison of the FLO-TFAR S Sα model based on fractional low order moment(FLOM) and the Gaussian TFAR model based on autocorrelation. Simulation show that the proposed FLO-TFAR algorithm can carry out high-resolution spectrum estimation, provided better performance than the TFAR algorithm, and is robust.