Time-varying behavior of the PARCOR coefficients of lattice filters with nonstationary AR inputs

Authors:
Tarun Soni;James R. Zeidler;Walter H. Ku
Affiliations:
Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA;Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA and Naval Command, Control and Ocean Surveillance Center, San Diego, CA;Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
Year:
1993

Citing 2
Cited 0

Adaptive filter theory (2nd ed.)

Adaptive filter theory (2nd ed.)
Tracking model of an adaptive lattice filter for a linear chirp signal in noise

ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference

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Abstract

This paper describes the behavior of the partial correlation coefficients of lattice filters based on the recursive least squares (RLS) algorithm in the presence of a nonstationary input. The input is an autoregressive process and the coefficients of the generating filter are allowed to change with time, leading to a time varying autocorrelation function at the input. For such an input the optimal Wiener-Hopf coefficients of a lattice filter are found. These are then compared with the expected PARCOR coefficients of the recursive least squares lattice filter which are a function of the weighting parameter. It is shown that the PARCOR coefficients of the RLS lattice filter have two terms and tend to the Wiener-Hopf optimal weights asymptotically.