The Kalman filter is another one of the real‐time computational algorithms for state estimation. This chapter introduces mathematical models used in the Kalman filter followed by a derivation of the filter. The derivation follows an intuitive approach, although there are a number of ways to derive the Kalman filter. The chapter discusses the compensation of sensor bias and load disturbance. The commonly encountered scenarios in the Kalman filter applications include multi‐rate sampled data and missing measurements. The chapter presents the extended Kalman filter for state estimation of nonlinear systems where several examples are given together with MATLAB tutorials. The Kalman filter algorithm has the capacity to handle missing data and to operate in a multi‐rate sampling environment. The chapter explains the idea of prescribed degree of stability in discrete‐time linear quadratic regulator to the Kalman filter, leading to the Kalman filter with fading memory. It discusses the relationship between the Kalman filter and an observer.
The Kalman Filter
2022-12-19
66 pages
Article/Chapter (Book)
Electronic Resource
English
Wiley | 2022
|Wiley | 2019
|AIAA | 2015
|Wiley | 2000
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