AUTOREGRESSIVE (AR) AND AUTOREGRESSIVE MOVING AVERAGE (ARMA) SPECTRALESTIMATION TECHNIQUES FOR FASTER TLM ANALYSIS OF MICROWAVE STRUCTURES

Autoregressive (AR) and autoregressive moving average (ARMA) technique s have been successfully implemented in conjunction with the transmiss ion line matrix (TLM) method for efficient time-domain analysis of mic rowave structures. The AR technique can be used to compute the full ti me-domain response from a relatively short segment of the early TLM re sponse. It was found that the least-square technique of estimating the AR parameters requires a shorter time record than solving Yule-Walker equations through the Levinson-Durbin algorithm. The ARMA technique c an be used to compute the scattering parameters of microwave structure s without using the discrete Fourier transform. A recursive least squa re covariance ladder algorithm has been used for ARMA modeling. Both A R and ARMA models have been validated by applying them to waveguide an d suspended substrate stripline filters. With these techniques, the sp eed of the computationally intensive TLM algorithm can be increased up to five times.