Robust Wiener filtering based on probabilistic descriptions of model errors. (English) Zbl 0814.93065
The paper deals with the problem of robust estimation of signals and prediction of time-series. A robust design is obtained by minimizing the squared estimations error with respect to model errors and noise. Model errors are described by sets of systems, parameterized by random variables with known covariances. A polynomial solution, based on averaged spectral factorizations and averaged Diophantine equations, is derived. In principle, the introduced methodology can be applied to any open loop filtering or control problem. The results are illustrated by a numerical example.
Reviewer: B.Mathiszik (Halle)
MSC:
| 93E11 | Filtering in stochastic control theory |
| 93E10 | Estimation and detection in stochastic control theory |
| 93B35 | Sensitivity (robustness) |
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