A generalized Kalman filter with its precision in recursive form when the stochastic model is misspecified

P. J.G. Teunissen*, A. Khodabandeh, D. Psychas

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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Abstract

In this contribution, we introduce a generalized Kalman filter with precision in recursive form when the stochastic model is misspecified. The filter allows for a relaxed dynamic model in which not all state vector elements are connected in time. The filter is equipped with a recursion of the actual error-variance matrices so as to provide an easy-to-use tool for the efficient and rigorous precision analysis of the filter in case the underlying stochastic model is misspecified. Different mechanizations of the filter are presented, including a generalization of the concept of predicted residuals as needed for the recursive quality control of the filter.
Original languageEnglish
Article number108
Number of pages12
JournalJournal of Geodesy
Volume95
Issue number9
DOIs
Publication statusPublished - 2021

Keywords

  • Error-variance matrices
  • Generalized filter
  • Kalman filter
  • Minimal detectable bias (MDB)
  • Predicted residual
  • Stochastic model

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