Improved Kalman filter initialisation using neurofuzzy estimation

Roberts, J.M., Mills, D.J., Charnley, D., & Harris, C.J. (1995) Improved Kalman filter initialisation using neurofuzzy estimation. In Proceedings of the Fourth International Conference on Artificial Neural Networks, 1995, IET, Cambridge, United Kingdom, pp. 329-334.

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Abstract

It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs, but unfortunately their performance is extremely sensitive to state initialisation accuracy: good initial state estimates ensure fast convergence whereas poor estimates may give rise to slow convergence or even filter divergence. Divergence is generally due to excessive observation noise and leads to error magnitudes that quickly become unbounded (R.J. Fitzgerald, 1971). When a filter diverges, it must be re initialised but because the observations are extremely poor, re initialised states will have poor estimates. The paper proposes that if neurofuzzy estimators produce more accurate state estimates than those calculated from the observed noisy inputs (using the known state model), then neurofuzzy estimates can be used to initialise the states of Kalman and extended Kalman filters. Filters whose states have been initialised with neurofuzzy estimates should give improved performance by way of faster convergence when the filter is initialised, and when a filter is re started after divergence

Impact and interest:

3 citations in Scopus
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ID Code: 83465
Item Type: Conference Paper
Refereed: Yes
Keywords: Kalman filters, Estimation theory, Fuzzy neural nets, Fuzzy set theory, Signal processing
DOI: 10.1049/cp:19950577
ISBN: 0852966415
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 09 Apr 2015 23:16
Last Modified: 09 Apr 2015 23:16

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