Abstract:
The paper extends the Kalman filter to operate with the potential process model uncertainty by relying on the use of a variable fading factor. A loss functional evaluatin...Show MoreMetadata
Abstract:
The paper extends the Kalman filter to operate with the potential process model uncertainty by relying on the use of a variable fading factor. A loss functional evaluating the prediction step of the Kalman filter is constructed based on Bayesian decision-making. This evaluation results in coupling two normal probability density functions (pdfs), defining a lower and upper bound for a state uncertainty increase. The coupling policy is identical with the geometric mean of pdfs weighted by adaptively adjusted probabilities. In this respect, the fading factor is optimally determined by being treated as a probability assigned to the more conservative pdf. The proposed schema corrects state filtering in the presence of model uncertainty through controlling the Kalman gain matrix in response to observed performance.
Published in: 2018 Annual American Control Conference (ACC)
Date of Conference: 27-29 June 2018
Date Added to IEEE Xplore: 16 August 2018
ISBN Information:
Electronic ISSN: 2378-5861