Bibliography

(eng): The paper deals with parameter and state estimation and focuses on two problems that frequently occur in many practical applications: (i) bounded uncertainty and (ii) missing measurement data. An algorithm for the state estimation of the discrete-time non-linear state space model whose uncertainties are bounded is proposed. The algorithm also copes with situations when some measurements are missing. It uses Bayesian approach and evaluates maximum a posteriori probability (MAP) estimates of states and parameters. As the model uncertainties are supposed to have a bounded support, the searched estimates lie within an area that is described by the system of inequalities. In consequence, the problem of MAP estimation becomes the problem of nonlinear mathematical programming (NLP). The estimation with missing data reduces to the omission of corresponding inequalities in NLP formulation.