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Federated Adaptive Kalman Filtering and its application

  • Long Zhao*
  • *Corresponding author for this work

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

Abstract

In order to deal with the problem in which the Federated Kalman Filtering (FKF) may be instable or divergent when noise statistics is unknown, a new federated filtering is presented, which is defined as Federated Adaptive Kalman Filtering (FAKF). A factor of modified the measurement noise covariance was built by using the ratio between filter residual and actual residual in FAKF. The adaptive estimation of FKF was realized by online modifying the measurement noise covariance. FAKF and FKF were compared using practical measuring data in inertial navigation system/global positioning system/double-star system (INS/GPS/DS) integrated navigation system. Simulation results show that FAKF has adaptability and has better estimation accuracy than the FKF when noise statistics information is unknown.

Original languageEnglish
Title of host publicationProceedings of the 7th World Congress on Intelligent Control and Automation, WCICA'08
Pages1369-1372
Number of pages4
DOIs
StatePublished - 2008
Event7th World Congress on Intelligent Control and Automation, WCICA'08 - Chongqing, China
Duration: 25 Jun 200827 Jun 2008

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)

Conference

Conference7th World Congress on Intelligent Control and Automation, WCICA'08
Country/TerritoryChina
CityChongqing
Period25/06/0827/06/08

Keywords

  • Adaptive filtering
  • Federated filtering
  • Integrated navigation system
  • Satellite positioning system

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