Abstract
Low-quality models that miss relevant dynamics lead to major challenges in model-based state estimation. We address this issue by simultaneously estimating the system's states and its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely, we augment the state with the parameter vector of a linear combination containing suitable functions that approximate the lacking dynamics. Presuming that only a few dynamical terms are relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like sparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace distribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF, the regularized horseshoe distribution, a Gaussian that approximately features sparsity, is applied instead. Results exhibit small estimation errors with model improvements detected by an automated model reduction technique.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.