Robust filter design for uncertain systems defined by both hard and soft bounds

A new approach to robust linear filter design is described that attempts to combine the advantages of H/sub /spl infin// robust linear synthesis with a probabilistic method of system and noise modeling. The signal and measurement noise model parameters are assumed to be subject to perturbations repr...

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Bibliographic Details
Published in:IEEE transactions on signal processing 1996-05, Vol.44 (5), p.1063-1071
Main Author: Grimble, M.J.
Format: Article
Language:English
Subjects:
Applied sciences
Deconvolution
Detection, estimation, filtering, equalization, prediction
Equations
Exact sciences and technology
Filtering
Information, signal and communications theory
Noise measurement
Noise robustness
Nonlinear filters
Polynomials
Random variables
Signal and communications theory
Signal synthesis
Signal, noise
Telecommunications and information theory
Uncertain systems
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Summary:A new approach to robust linear filter design is described that attempts to combine the advantages of H/sub /spl infin// robust linear synthesis with a probabilistic method of system and noise modeling. The signal and measurement noise model parameters are assumed to be subject to perturbations represented by random variables with known covariances. The system is represented in polynomial form, and the uncertainty can be described by both soft and hard bounds. An H/sub /spl infin// cost-function is minimized and averaged with respect to model errors in signal and noise descriptions. The polynomial solution is no more complicated than the usual H/sub /spl infin// optimal filter and involves averaged spectral factorizations and linear equations. Both usual and deconvolution filtering problems are considered.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.502320