Bahadır Aybar - Academia.edu (original) (raw)

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ROBUST MINIMAX ESTIMATION APPLIED TO KALMAN FILTERING Bahadir Aybar M.S. in Electrical and Electr... more ROBUST MINIMAX ESTIMATION APPLIED TO KALMAN FILTERING Bahadir Aybar M.S. in Electrical and Electronics Engineering Supervisor: Prof. Dr. Orhan Arikan September 2008 Kalman filtering is one of the most essential tools in estimating an unknown state of a dynamic system from measured data, where the measurements and the previous states have a known relation with the present state. It has generally two steps, prediction and update. This filtering method yields the minimum mean-square error when the noise in the system is Gaussian and the best linear estimate when the noise is arbitrary. But, Kalman filtering performance degrades significantly with the model uncertainty in the state dynamics or observations. In this thesis, we consider the problem of estimating an unknown vector x in a statespace model that may be subject to uncertainties. We assume that the model uncertainty has a known bound and we seek a robust linear estimator for x that minimizes the worst case mean-square error acr...

ROBUST MINIMAX ESTIMATION APPLIED TO KALMAN FILTERING Bahadir Aybar M.S. in Electrical and Electr... more ROBUST MINIMAX ESTIMATION APPLIED TO KALMAN FILTERING Bahadir Aybar M.S. in Electrical and Electronics Engineering Supervisor: Prof. Dr. Orhan Arikan September 2008 Kalman filtering is one of the most essential tools in estimating an unknown state of a dynamic system from measured data, where the measurements and the previous states have a known relation with the present state. It has generally two steps, prediction and update. This filtering method yields the minimum mean-square error when the noise in the system is Gaussian and the best linear estimate when the noise is arbitrary. But, Kalman filtering performance degrades significantly with the model uncertainty in the state dynamics or observations. In this thesis, we consider the problem of estimating an unknown vector x in a statespace model that may be subject to uncertainties. We assume that the model uncertainty has a known bound and we seek a robust linear estimator for x that minimizes the worst case mean-square error acr...

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