title: Linear generalized Kalman-Bucy filter
metadata.name: dc.contributor.author
metadata.value: Tovstik, Tatiana M.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Tovstik, Petr E.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2019-12-06T12:54:25Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2019-12-06T12:54:25Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2019-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Tovstik T.M., Tovstik P.E. Linear generalized Kalman-Bucy filter. Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy, 2019, vol. 6 (64), issue 4, pp. 636–645.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu01.2019.409
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/16746
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The linear generalized Kalman  Bucy filter problem is studied. An observed process is
  a sum of a useful signal and a noise. A signal and a noise are independent stationary
  auto-regressive processes with orders exceeding 1. The filter estimates a signal by using an
  observed process. Two algorithms of filter are considered, recurrent and direct. In frames
  of the recurrent algorithm to find the next in turn estimation of a signal the current
  observation and some last previous filter estimations are used. The direct algorithm uses
  all previous observations directly. For the both algorithms the errors of estimation are
  found. The advantages and locks of both algorithms are discussed. Calculations at the recurrent algorithm does depend on time of observation. The direct algorithm is reduced
  to a linear algebraic system, order of that increases with a time. On the other side, the
  direct algorithm converges with growth of time in all cases, and the recurrent algorithm
  sometime may not converge. Numerical examples are given.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: The article was prepared with the financial support of the Russian Foundation for Basic Research (RFBR), project No. 17-01-00267а.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
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: Vestnik of St Petersburg University. Mathematics. Mechanics. Astronomy;Volume 6 (64); Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: Kalman-Bucy filter
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: recurrent and direct algorithms
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: high order auto-regressive processes
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Linear generalized Kalman-Bucy filter
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 636-645.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/16746/1/636-645.pdf
resources.format: Adobe PDF
resources.size: 481,94 kB