Estimation Methods Based on Kalman Filtering

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Estimation Methods Based on Kalman Filtering

Doumiati, Moustapha / Charara, Ali / Victorino, Alessandro / Lechner, Daniel / Dubuisson, Bernard

This chapter estimates sequentially the state of the vehicle dynamic system using a sequence of noisy available measurements made on the system. The state‐space approach in the discrete‐time formulation is adopted when modeling. For dynamic state estimation, the discrete‐time approach is both widespread and convenient for real‐time application using onboard systems. The state space representation focuses attention on the state vector of a system. The state vector assumes that all relevant information required to describe the system is contained. The chapter presents the observability concept for linear and nonlinear systems, and then it formulates the equations of linear, extended and unscented Kalman filters (UKFs).

Kalman filters; vehicle dynamics

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Document information

Title :

Estimation Methods Based on Kalman Filtering

Contributors:

Doumiati, Moustapha (editor) / Charara, Ali (editor) / Victorino, Alessandro (editor) / Lechner, Daniel (editor) / Dubuisson, Bernard (editor) / Doumiati, Moustapha (author) / Charara, Ali (author) / Victorino, Alessandro (author) / Lechner, Daniel (author) / Dubuisson, Bernard (author)

Published in:

Vehicle Dynamics Estimation using Kalman Filtering ; 37-60

Publication date :

2012-12-17

Size :

24 pages

ISBN :

9781848213661 , 9781118578988

DOI :

https://doi.org/10.1002/9781118578988.ch2

Type of media :

Article/Chapter (Book)