Titlebook: Kalman Filtering; with Real-Time Appli Charles K. Chui,Guanrong Chen Textbook 2017Latest edition Springer International Publishing AG 2017

BROOK

Kalman Filtering for Interval Systems,ng algorithm cannot be directly applied. In this case, robust Kalman filtering that has the ability of handling uncertainty is needed. In this chapter we introduce one of such robust Kalman filtering algorithms.

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978-3-319-83780-2Springer International Publishing AG 2017

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描绘

Extended Kalman Filter and System Identification, procedure is usually performed in deriving the filtering equations. We will consider a real-time linear Taylor approximation of the system function at the previous state estimate and that of the observation function at the corresponding predicted position.

断言

Kalman Filtering for Interval Systems,ng algorithm cannot be directly applied. In this case, robust Kalman filtering that has the ability of handling uncertainty is needed. In this chapter we introduce one of such robust Kalman filtering algorithms.

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Preliminaries,The importance of Kalman filtering in engineering applications is well known, and its mathematical theory has been rigorously established. The main objective of this treatise is to present a thorough discussion of the mathematical theory, computational algorithms, and application to real-time tracking problems of the Kalman filter.

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Correlated System and Measurement Noise Processes,In the previous two chapters, Kalman filtering for the model involving uncorrelated system and measurement noise processes was studied.

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Colored Noise Setting,Consider the linear stochastic system with the following state-space description.

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Sequential and Square-Root Algorithms,It is now clear that the only time-consuming operation in the Kalman filtering process is the computation of the Kalman gain matrices.