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On Gaussian Optimal Smoothing of Non-Linear State Space Models

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2 Author(s)
Sarkka, S. ; Aalto Univ., Aalto, Finland ; Hartikainen, J.

In this note we shall present a new Gaussian approximation based framework for approximate optimal smoothing of non-linear stochastic state space models. The approximation framework can be used for efficiently solving non-linear fixed-interval, fixed-point and fixed-lag optimal smoothing problems. We shall also numerically compare accuracies of approximations, which are based on Taylor series expansion, unscented transformation, central differences and Gauss-Hermite quadrature.

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Automatic Control, IEEE Transactions on  (Volume:55 ,  Issue: 8 )