0000000000437542
AUTHOR
Juergen Sachau
Using an Adaptive High-Gain Extended Kalman Filter With a Car Efficiency Model
The authors apply the Adaptive High-Gain Extended Kalman Filter (AEKF) to the problem of estimating engine efficiency with data gathered from normal driving. The AEKF is an extension of the traditional Kalman Filter that allows the filter to be reactive to perturbations without sacrificing noise filtering. An observability normal form of the engine efficiency model is developed for the AEKF. The continuous-discrete AEKF is presented along with strategies for dealing with asynchronous data. Empiric test results are presented and contrasted with EKF-derived results.Copyright © 2010 by ASME
Adaptive-gain extended Kalman filter: application to a series connected DC motor
International audience
High-gain observers and Kalman filtering in hard real-time
International audience