2020/03/16 by Anton Kullberg, Kullberg, Anton, Isaac Skog +3
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Class (philosophy) #Computer science #Control Systems and Identification #Domain (mathematical analysis) #Engineering #FOS: Electrical engineering #Fault Detection and Control Systems #Gaussian #Gaussian Processes and Bayesian Inference #Gaussian process #Inference #Intersection (aeronautics) #Machine learning #Mathematics #Position (finance) #Process (computing) #Set (abstract data type) #Signal Processing (eess.SP) #Space (punctuation) #State (computer science) #State space #State-space representation #Statistics #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2003.07312
published in arXiv (Cornell University) (Cornell University) · 7 pages, 6 figures. Submitted to FUSION 2020
arxiv created 2020/03/16 · openalex publication_date 2020/03/16 · arxiv updated 2020/03/17 · openalex created_date 2022/07/23 · openalex updated_date 2026/08/08
An inference method for Gaussian process augmented state-space models are\npresented. This class of grey-box models enables domain knowledge to be\nincorporated in the inference process to guarantee a minimum of performance,\nstill they are flexible enough to permit learning of partially unknown model\ndynamics and inputs. To facilitate online (recursive) inference of the model a\nsparse approximation of the Gaussian process based upon inducing points is\npresented. To illustrate the application of the model and the inference method,\nan example where it is used to track the position and learn the behavior of a\nset of cars passing through an intersection, is presented. Compared to the case\nwhen only the state-space model is used, the use of the augmented state-space\nmodel gives both a reduced estimation error and bias.\n