Journal article · Conference paper
Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated by the UKF are used for covariance estimation by MLE and CM.
Then we apply the two covariance estimation methods on an example application. In the example, we identify the covariance of the measurement noise for a continuous glucose monitoring (CGM) sensor. The sensor measures the subcutaneous glucose concentration for a type 1 diabetes patient. The root-mean square (RMS) error and the computation time are used to compare the performance of the two covariance estimation methods.
The results indicate that as the prediction horizon expands, the RMS error for the MLE declines, while the error remains relatively large for the CM method. For larger prediction horizons, the MLE provides an estimate of the noise covariance that is less biased than the estimate by the CM method. The CM method is computationally less expensive though.
Language: | English |
---|---|
Year: | 2017 |
Pages: | 3859-3864 |
Proceedings: | 20th World Congress of the International Federation of Automatic Control |
ISSN: | 24058963 and 24058971 |
Types: | Journal article and Conference paper |
DOI: | 10.1016/j.ifacol.2017.08.356 |
ORCIDs: | Mahmoudi, Zeinab , Poulsen, Niels Kjølstad , Madsen, Henrik and Jørgensen, John Bagterp |