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Online tests of Kalman filter consistency

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Details

Original languageEnglish
Pages (from-to)115–124
JournalInternational Journal of Adaptive Control and Signal Processing
Volume30
Issue number1
DOIs
Publication statusPublished - 2016
Publication typeA1 Journal article-refereed

Abstract

The normalised innovation squared (NIS) test, which is used to assess whether a Kalman filter's noise assumptions are consistent with realised measurements, can be applied online with real data, and does not require future data, repeated experiments or knowledge of the true state. In this work, it is shown that the NIS test is equivalent to three other model criticism procedures, which are as follows: (i) it can be derived as a Bayesian p-test for the prior predictive distribution; (ii) as a nested-model parameter significance test; and (iii) from a recently-proposed filter residual test. A new NIS-like test corresponding to a posterior predictive Bayesian p-test is presented.

Keywords

  • Kalman filter, Model consistency, Normalised innovations squared, Predictive distribution

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