Abstract
The stochastic state space model introduced in Section 2.4 is an internal model: its states x k are not observed directly but do contribute to the observed outputs y k as specified by the observation equation in (2.4.3). It is natural then to consider the problem of forming ‘best estimates’ of the state x k give the available data (y O, y 1,…,y k ). This procedure is known as filtering. There are at least three situations in which filtering is required. Firstly, it may be an end in itself: this is the case when, as often happens, the state variables xi k represent important physical quantities in a system which we need to know as accurately as possible even though they cannot be measured directly. Secondly, if we wish to control systems described by state space models then the natural class of controls to consider is that of state feedback controls where the control variable uk takes the form u k = u(k, x k ). If x k is not ‘known’ then in some circumstances it can be replaced by a best estimate x k produced by filtering; this topic is described at length in Chapter 6. Finally, filtering is relevant when we wish to replace the state space model by an ‘equivalent’ external model; see section 3.4 below.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, B. D. O. and Moore, J. B. (1979) Optimal Filtering, Prentice-Hall, Englewood Cliffs, NJ.
Box, G. E. P. and Jenkins, G. M. (1976) Time Series Analysis, Forecasting and Control, 2nd edn, Holden-Day, San Francisco.
Gelb, A. (ed.) (1974) Applied Optimal Estimation, MIT Press, Cambridge, Mass.
Kailath, T. (1980) Linear Systems, Prentice-Hall, Englewood Cliffs, NJ.
Kalman, R. E. (1960) A new approach to linear filtering and prediction problems. ASME Transactions, Part D (J. of Basic Engineering), 82, 35 - 45.
Kalman, R. E. and Bucy, R. S. (1961) New results in linear filtering and prediction theory. ASME Transactions, Part D (J. of Basic Engineering), 83, 95 - 108.
Kolmogorov, A. N. (1941) Interpolation und Extrapolation von Stationaren Zufalligen Folgen. Bull Acad. Sci. URSS, Sér. Math. 5, 3 - 14.
Maybeck, P. S. (1979) Stochastic Models, Estimation and Control, Vol. 1, Academic Press, NY.
Pappas, T., Laub, A, J. and Sandell, N. R. (1980) On the numerical solution of the algebraic Riccati equation. IEEE Trans. Automatic Control AC-25, 631–641.
Wiener, N. (1949) Extrapolation, Interpolation and Smoothing of StationaryTime Series, MIT Press.
Wiener, N. and Masani, P. The prediction theory of multivariable stochastic processes I and II. Acta Math. (1957) 98, 111-150; (1958) 99, 93 - 137.
Wold, H. (1938) A Study in the Analysis of Stationary Time Series, Almqvist and Wiksell, Stockholm.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1985 M. H. A. Davis and R. B. Vinter
About this chapter
Cite this chapter
Davis, M.H.A., Vinter, R.B. (1985). Filtering theory. In: Stochastic Modelling and Control. Monographs on Statistics and Applied Probability. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-4828-0_3
Download citation
DOI: https://doi.org/10.1007/978-94-009-4828-0_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-8640-0
Online ISBN: 978-94-009-4828-0
eBook Packages: Springer Book Archive