Unscented Kalman filter for arbitrary step randomly delayed measurements

Ajay Kumar Yadav, Vikas Kumar Mishra, Abhinoy Kumar Singh, Shovan Bhaumik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

The conventional Bayesian framework of filtering is based on the assumption that the measurements are available at each time-step without any delay. But in real-life problems, measurements may be randomly delayed in time. In this paper, we modified the unscented Kalman filter (UKF) for arbitrary time delayed measurements. With the help of simulation results, it has been shown that the proposed filter provides more accurate estimation compared to the ordinary UKF in presence of randomly delayed measurements.

Original languageEnglish
Title of host publication2017 Indian Control Conference, ICC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-86
Number of pages5
ISBN (Electronic)9781509017959
DOIs
StatePublished - 7 Feb 2017
Event3rd Indian Control Conference, ICC 2017 - Guwahati, India
Duration: 4 Jan 20176 Jan 2017

Publication series

Name2017 Indian Control Conference, ICC 2017 - Proceedings

Conference

Conference3rd Indian Control Conference, ICC 2017
Country/TerritoryIndia
CityGuwahati
Period4/01/176/01/17

ASJC Scopus subject areas

  • Applied Mathematics
  • Control and Optimization

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