Sensitivity analysis of discrete Kalman filters

Abstract
This paper investigates the sensitivity of discrete Kalman filters to erroneous models. Both parameter and structure (state dimensionality) sensitivity are considered, as well as deterministic and random parameter errors. Iterative algorithms are derived for the calculation of the actual filter error covariance matrix for the case of known (deterministic) modelling errors. For the case of random statistical and dynamical modelling errors, an optimal mean-square error estimate of the actual system performance is derived.

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