2. Estimation Methods Based on Kalman Filtering

  1. Moustapha Doumiati,
  2. Ali Charara,
  3. Alessandro Victorino,
  4. Daniel Lechner and
  5. Bernard Dubuisson
  1. Moustapha Doumiati,
  2. Ali Charara,
  3. Alessandro Victorino,
  4. Daniel Lechner and
  5. Bernard Dubuisson

Published Online: 26 DEC 2012

DOI: 10.1002/9781118578988.ch2

Vehicle Dynamics Estimation using Kalman Filtering

Vehicle Dynamics Estimation using Kalman Filtering

How to Cite

Doumiati, M., Charara, A., Victorino, A., Lechner, D. and Dubuisson, B. (2012) Estimation Methods Based on Kalman Filtering, in Vehicle Dynamics Estimation using Kalman Filtering (eds M. Doumiati, A. Charara, A. Victorino, D. Lechner and B. Dubuisson), John Wiley & Sons, Inc., Hoboken, NJ USA. doi: 10.1002/9781118578988.ch2

Publication History

  1. Published Online: 26 DEC 2012
  2. Published Print: 17 DEC 2012

ISBN Information

Print ISBN: 9781848213661

Online ISBN: 9781118578988

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Keywords:

  • linear Kalman filter;
  • State-space representation;
  • unscented Kalman filter

Summary

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).

Controlled Vocabulary Terms

Kalman filters; vehicle dynamics