The Kalman filter is mainly based on the concept of covariance matrix, which is important to grasp in order to understand the design and the utilization of the observer. This chapter describes the fundamental concepts surrounding covariance matrices. It presents a theorem that shows that the unbiased orthogonal linear estimator is the best among all unbiased estimators. The linear estimator can be used to solve problems that can be translated as linear equations. The Kalman filter is used in numerous mobile robotics applications, even though the robots in question are strongly nonlinear. The Kalman filter alternates between two phases: correction and prediction. The Kalman–Bucy filter corresponds to a continuous version of the Kalman filter. It allows people to understand some effects that occur when the discrete‐time Kalman filter is used for continuous‐time systems.
Kalman Filter
Mobile Robotics ; 263-327
2019-09-30
65 pages
Article/Chapter (Book)
Electronic Resource
English
Wiley | 2022
|Wiley | 2000
|British Library Online Contents | 2017
|AIAA | 2015
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