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Robustness in adaptive filtering: How much is enough?

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3 Author(s)
P. Bolzern ; Dipt. di Elettronica, Politecnico di Milano, Italy ; P. Colaneri ; G. De Nicolao

The issue of robustness of adaptive filtering algorithms has been investigated in the literature using the H paradigm. In particular, in the constant parameter case, the celebrated (normalized) least mean squares (LMS) algorithm has been shown to coincide with the central H-filter ensuring the minimum achievable disturbance attenuation level. In this paper, the problem is re-examined by taking into account the robust performance of three classical algorithms (normalized LMS, Kalman filter, central H-filter) with respect to both measurement noise and parameter drift. It turns out that normalized LMS does not guarantee any finite level of H-robustness. On the other hand, it is shown that striving for the minimum achievable attenuation level leads to a trivial nondynamic estimator with poor H2-performance. This motivates the need for a design approach balancing H2 and H performance criteria

Published in:

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on  (Volume:5 )

Date of Conference:

10-12 Dec 1997