ML Estimation of Process Noise Variance in Dynamic Systems
2011 (English)In: Proceedings of the 18th IFAC World Congress, 2011, 5609-5614 p.Conference paper (Refereed)
The performance of a non-linear filter hinges in the end on the accuracy of the assumed non-linear model of the process. In particular, the process noise covariance Q is hard to get by physical modeling and dedicated system identification experiments. We propose a variant of the expectation maximization (EM) algorithm which iteratively estimates the unobserved state sequence and Q based on the observations of the process. The extended Kalman smoother (EKS) is the instrument to find the unobserved state sequence. Our contribution fills a gap in literature, where previously only the linear Kalman smoother and particle smoother have been applied. The algorithm will be important for future industrial robots with more flexible structures, where the particle smoother cannot be applied due to the high state dimension. The proposed method is compared to two alternative methods on a simulated robot.
Place, publisher, year, edition, pages
2011. 5609-5614 p.
Robotic manipulators, Extended Kalman filters, Smoothing filters, Identification, Maximum likelihood, Covariance matrices
National CategoryControl Engineering
IdentifiersURN: urn:nbn:se:liu:diva-72219DOI: 10.3182/20110828-6-IT-1002.00543ISBN: 978-3-902661-93-7OAI: oai:DiVA.org:liu-72219DiVA: diva2:458439
18th IFAC World Congress, Milano, Italy, 28 August-2 September, 2011