title: Analysis of Algorithms of Numerical Implementations for the Wonham Filter Under Uncertainty in Measurements Noise Covariance
metadata.name: dc.contributor.author
metadata.value: Bosov, Alexey Vyacheslavovich
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kudryavtseva, Irina Anatol'evna
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-12-02T15:59:20Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-12-02T15:59:20Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2024-09
metadata.lang: -
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu35.2024.303
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/48056
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The paper addresses the filtering a continuous-time Markov chain states that can be observed through linear measurements perturbed by a Wiener process. There is supposed the presence of uncertainty in the intensity of measurements noises. The problem is worked out under the assumption of unknown intensity but subject to its known upper bound. If there is no uncertainty in measurements the optimal solution is provided by the Wonham filter that doesn't ensure stable numerical implementations. The paper exposes that the Wonham filter shows robustness in the presence of uncertainty if model's parameters don't imply its divergent. It is detected that to cope with divergence tracking and handling trajectories aren't sufficient in the case of uncertainty. The more efficient way is to consider discretized approximations of the Wonham filter implemented for a discrete model that approximates the initial continuous-time measurements system. Such an approach perceptibly advantages if numerical implementations contain divergent trajectories. If there are no divergent trajectories, then the discretized filters give a slightly worse result but acceptable.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Differential Equations and Control Processes;Issue 3
metadata.lang: -
metadata.name: dc.subject
metadata.value: filtering problem
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: nonlinear filtering
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: Wonham filter
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: minimax estimation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: Markov chain
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Analysis of Algorithms of Numerical Implementations for the Wonham Filter Under Uncertainty in Measurements Noise Covariance
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 24303-jdecp-kudryavtseva.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/48056/1/24303-jdecp-kudryavtseva.pdf
resources.format: Adobe PDF
resources.size: 1,5 MB