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The paper deals with state estimation of stochastic nonlinear dynamical systems. A structure adaptation of nonlinear filters is proposed to reduce errors stemming from approximations made by the filters. The adaptation is controlled by non-Gaussian measures which assess current working conditions of the filter. A large non-Gaussian measure indicates a possible large approximation error and results...
The paper deals with state estimation of stochastic nonlinear systems by means of local filters. A new technique is designed to provide a self-assessment of the filter with respect to its estimate quality. It uses a non-Gaussianity measure based on conditional third moment of the state to indicate a possible decrease of estimate quality. The technique is proposed for general local filters with detailed...
The paper deals with state estimation of nonlinear stochastic dynamic systems. In particular, the stress is laid on the recursive computation of the higher order moments of the state estimate in a local filter. The higher order moments are then used as a cornerstone of non-Gaussianity measures having the ability to indicate a possible decrease of the estimate quality. A technique is proposed for a...
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