On the extended RLS adaptive bilinear filters

Authors:
Junghsi Lee;V. John Mathews
Affiliations:
Advanced Technology Center, Computer and Communication Research Laboratories, Industrial Technology Research Institute, Chutung, Hsinchu, Taiwan, Taiwan, R.O.C.;Department of Electrical Engineering, University of Utah, Salt Lake City, Utah
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
Year:
1993

Citing 4
Cited 0

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Adaptive lattice bilinear filters

IEEE Transactions on Signal Processing

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Abstract

This paper considers recursive least squares (RLS) adaptive nonlinear filtering using bilinear system models. It proves that the extended RLS adaptive bilinear filter as well as the equation-error RLS adaptive bilinear filter are guaranteed to be stable in the sense that the time average of the squared estimation error is bounded whenever the underlying process that generates the input signals is stable in the same sense. This paper also contains the results of several simulation experiments that compare the usefulness of adaptive bilinear system models with that of truncated second-order Volterra system models in a communication system problem.