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This paper introduces the particle-filtering (PF) based framework for fault diagnosis in non-linear systems and noise and disturbances being Gaussian. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distribution of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution...
In this paper we present a toolbox enabling easy evaluation and comparison of different filtering algorithms. The toolbox is called Kalmtool 4 and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox contains functions for extended Kalman filtering as well as for DD1 filter and the DD2 filter. It also contains functions for Unscented Kalman filters as well as several versions...
This paper deals with recursive state estimation for nonlinear systems. A new set of sigma-points for the unscented Kalman filter is proposed as well as a way to substitute a nonlinear output with a linear one. The importance of the function of the state which must be estimated is also illustrated and the need for taking it into account when designing the state estimator. All the proposed methods...
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