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In many contemporary engineering problems, model uncertainty is inherent because accurate system identification is virtually impossible owing to system complexity or lack of data on account of availability, time, or cost. The situation can be treated by assuming that the true model belongs to an uncertainty class of models. In this context, an intrinsically Bayesian robust (IBR) filter is one that...
In this paper, we propose a Bayesian framework for robust Kalman filtering when noise statistics are unknown. The proposed intrinsically Bayesian robust Kalman filter is robust in the Bayesian sense meaning that it guarantees the best average performance relative to the prior distribution governing unknown noise parameters. The basics of Kalman filtering such as the projection theorem and the innovation...
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