Adaptive algorithms for bilinear filtering

V. John Mathews; Junghsi Lee

doi:10.1117/12.190846

28 October 1994 Adaptive algorithms for bilinear filtering

V. John Mathews, Junghsi Lee

Proceedings Volume 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V; (1994) https://doi.org/10.1117/12.190846
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

This paper presents an overview of adaptive nonlinear filters equipped with bilinear system models. Bilinear filters are recursive nonlinear systems that belong to the class of polynomial systems. Because of the feedback structure, such models are able to represent many nonlinear systems efficiently. The paper first describe stochastic gradient adaptive bilinear filters. The class of recursive least-squares adaptive bilinear filters are discussed next. Stability issues associated with bilinear system models and adaptive bilinear filters are also discussed in the paper. The paper concludes with an experimental comparison of the performance of linear, truncated second-order Volterra, and bilinear system models in a nonlinear channel equalization problem.

Citation Download Citation

V. John Mathews and Junghsi Lee "Adaptive algorithms for bilinear filtering", Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); https://doi.org/10.1117/12.190846

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