SSP'05 -- Proceedings

	IEEE/SP 13th workshop on Statistical Signal Processing July, 17-20, 2005 - Bordeaux - France

Information regarding the paper

Title

Linear Methods for TFARMA Parameter Estimation and System Approximation

Author(s)

Michael Jachan	Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology
Franz Hlawatsch	Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology
Gerald Matz	Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology

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Abstract

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.