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Online tests of Kalman filter consistency

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Online tests of Kalman filter consistency. / Piché, Robert.

In: International Journal of Adaptive Control and Signal Processing, Vol. 30, No. 1, 2016, p. 115–124.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Piché, R 2016, 'Online tests of Kalman filter consistency', International Journal of Adaptive Control and Signal Processing, vol. 30, no. 1, pp. 115–124. https://doi.org/10.1002/acs.2571

APA

Piché, R. (2016). Online tests of Kalman filter consistency. International Journal of Adaptive Control and Signal Processing, 30(1), 115–124. https://doi.org/10.1002/acs.2571

Vancouver

Piché R. Online tests of Kalman filter consistency. International Journal of Adaptive Control and Signal Processing. 2016;30(1):115–124. https://doi.org/10.1002/acs.2571

Author

Piché, Robert. / Online tests of Kalman filter consistency. In: International Journal of Adaptive Control and Signal Processing. 2016 ; Vol. 30, No. 1. pp. 115–124.

Bibtex - Download

@article{bd89d42718634f3b97fb21fe2f8195fc,
title = "Online tests of Kalman filter consistency",
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",
author = "Robert Pich{\'e}",
year = "2016",
doi = "10.1002/acs.2571",
language = "English",
volume = "30",
pages = "115–124",
journal = "International Journal of Adaptive Control and Signal Processing",
issn = "0890-6327",
publisher = "Wiley",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Online tests of Kalman filter consistency

AU - Piché, Robert

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Kalman filter

KW - Model consistency

KW - Normalised innovations squared

KW - Predictive distribution

U2 - 10.1002/acs.2571

DO - 10.1002/acs.2571

M3 - Article

VL - 30

SP - 115

EP - 124

JO - International Journal of Adaptive Control and Signal Processing

JF - International Journal of Adaptive Control and Signal Processing

SN - 0890-6327

IS - 1

ER -