Talks and Poster Presentations (with Proceedings-Entry):
M. Jachan, F. Hlawatsch, G. Matz:
"Linear Methods for TFARMA Parameter Estimation and System Approximation";
Talk: IEEE-SP Workshop on Statistical Signal Processing (SSP),
- 07-20-2005; in: "Proceedings of the 13th Statistical Signal Processing Workshop (SSP)",
Time-frequency autoregressive moving-average (TFARMA) models have recently been introduced as parsimonious parametric models for underspread nonstationary random processes. In this paper, we propose linear TFARMA and TFMA parameter estimators based on a high-order TFAR model. These estimators extend the Graupe--Krause--Moore and Durbin methods for time-invariant parameter estimation to underspread nonstationary processes. We also derive linear methods for approximating an underspread time-varying linear system by a TFARMA-type system. The linear equations obtained have Toeplitz/block-Toeplitz structure and thus can be solved efficiently by the Wax-Kailath algorithm. Simulation results demonstrate the performance of the proposed methods.
Online library catalogue of the TU Vienna:
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.