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Mathematical and Computer Modelling of Dynamical Systems
Methods, Tools and Applications in Engineering and Related Sciences
Volume 13, 2007 - Issue 3
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Original Articles

Extended Kalman filtering for fuzzy modelling and multi-sensor fusion

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Pages 251-266 | Published online: 15 May 2007

Figures & data

Figure 1. EKF block diagram.

Figure 1. EKF block diagram.

Figure 2. Multi-sensor fusion in mobile robot navigation.

Figure 2. Multi-sensor fusion in mobile robot navigation.

Figure 3. Nonlinear function approximated by the fuzzy model.

Figure 3. Nonlinear function approximated by the fuzzy model.

Figure 4. Fuzzy-rule base generated with input space partitioning.

Figure 4. Fuzzy-rule base generated with input space partitioning.

Figure 5. RMSE of the EKF (continuous line) and the Gauss–Newton (dashed line) algorithm.

Figure 5. RMSE of the EKF (continuous line) and the Gauss–Newton (dashed line) algorithm.

Figure 6. Mobile robot with odometric sensors.

Figure 6. Mobile robot with odometric sensors.

Figure 7. Orientation of the sonar i.

Figure 7. Orientation of the sonar i.

Figure 8. Desirable trajectory of the autonomous vehicle i.

Figure 8. Desirable trajectory of the autonomous vehicle i.

Figure 9. (a) Desirable trajectory (continuous line) and trajectory using EKF fusion based on odometric and sonar measurements (−.−). (b) Desirable orientation θ d of the mobile robot (continuous line) and real orientation θ (−.−) using EKF fusion.

Figure 9. (a) Desirable trajectory (continuous line) and trajectory using EKF fusion based on odometric and sonar measurements (−.−). (b) Desirable orientation θ d of the mobile robot (continuous line) and real orientation θ (−.−) using EKF fusion.

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