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Abstract

This chapter derives Kalman filters in two stages; first, for innovation models, and then for general state space models. The first stage also serves to introduce a particular way of summarizing information contained in a data set as an output of a Kalman filter, a topic also elaborated upon in Chapter 8. Finally, this chapter introduces a non-recursive method for solving the matrix Riccati equation needed to determine the optimal filter gain matrix.

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© 1987 Springer-Verlag Berlin Heidelberg

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Aoki, M. (1987). Kalman Filters. In: State Space Modeling of Time Series. Universitext. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-96985-0_7

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  • DOI: https://doi.org/10.1007/978-3-642-96985-0_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-17257-4

  • Online ISBN: 978-3-642-96985-0

  • eBook Packages: Springer Book Archive

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