Online tests of Kalman filter consistency

Summary 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 th...

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Bibliographic Details
Published in:International journal of adaptive control and signal processing 2016-01, Vol.30 (1), p.115-124
Main Author: Piche, Robert
Format: Article
Language:English
Subjects:
Bayesian analysis
Consistency
Copyrights
Equivalence
Kalman filter
Mathematical models
model consistency
Noise
normalised innovations squared
Online
predictive distribution
Signal processing
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Summary:Summary 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. Copyright © 2015 John Wiley & Sons, Ltd.
ISSN:0890-6327
1099-1115
DOI:10.1002/acs.2571