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Proceedings Paper

Adaptive lattice bilinear filters
Author(s): Heung Ki Baik; V. John Mathews; Robert T. Short
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Paper Abstract

This paper presents two fast least-squares lattice algorithms for adaptive nonlinear filters equipped with bilinear system models. Bilinear models are attractive for adaptive filtering applications because they can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. The lattice filter formulation transforms the nonlinear filtering problem into an equivalent multichannel linear filtering problem and then uses multichannel lattice filtering algorithms to solve the nonlinear filtering problem. The first of the two approaches is an equation-error algorithm that uses the measured desired response signal directly to compute the adaptive filter outputs. This method is conceptually very simple however it will result in biased system models in the presence of measurement noise. The second approach is an approximate least-squares output-error solution. In this case the past samples of the output of the adaptive system itself are used to produce the filter output at the current time. This approach is expected to reduce the effect of measurement noise on the behavior of the system. Results of several experiments that demonstrate and compare the properties of the adaptive bilinear filters are also presented in the paper.

Paper Details

Date Published: 1 November 1990
PDF: 11 pages
Proc. SPIE 1348, Advanced Signal Processing Algorithms, Architectures, and Implementations, (1 November 1990); doi: 10.1117/12.23467
Show Author Affiliations
Heung Ki Baik, Univ. of Utah (United States)
V. John Mathews, Univ. of Utah (United States)
Robert T. Short, Univ. of Utah (United States)


Published in SPIE Proceedings Vol. 1348:
Advanced Signal Processing Algorithms, Architectures, and Implementations
Franklin T. Luk, Editor(s)

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