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

Abstract

The Parameter model analysis algorithms include autoregressive (AR) model, moving average (MA) and auto- regressive moving average (ARMA) model. The existing TFAR model is improved, the new fractional low order time- frequency autoregressive (FLO-TFAR) model and the concept of generalized TF-Yule-Walker equation are proposed, fractional low-order covariance is preferred instead of autocorrelation in the model; The parameter estimation of the mod- el 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 is done. Simulation shows that the proposed FLO-TFAR algo- rithm can carry out high-resolution spectrum estimation, provides better performance than the TFAR algorithm, and is ro- bust.