Abstract
One of the most significant ideas to emerge in the area of systems and control theory is the Kalman Filter. Given a signal model that consists of a linear dynamical system driven by stochastic white noise processes, the Kalman Filter provides a method for constructing an optimal estimate of the system state. Thus, the Kalman Filter provides an optimal way of extracting a signal from noise by exploiting a state space signal model. A key feature of the Kalman Filter is that it involves finite dimensional recursive computations, which are straightforward to implement on a digital computer. The Kalman Filter can be applied to state estimation problems defined over a finite or infinite time interval. It is applicable for the case in which the signal model is a linear but possibly time-varying linear system. Also, an approximate version of the Kalman Filter referred to as the “Extended Kalman Filter” can be applied in the case of a nonlinear signal model; e.g., see [2].
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© 1999 Springer Science+Business Media New York
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Petersen, I.R., Savkin, A.V. (1999). Introduction. In: Robust Kalman Filtering for Signals and Systems with Large Uncertainties. Control Engineering. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-1594-3_1
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DOI: https://doi.org/10.1007/978-1-4612-1594-3_1
Publisher Name: Birkhäuser, Boston, MA
Print ISBN: 978-1-4612-7209-0
Online ISBN: 978-1-4612-1594-3
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