Improved kalman filter initialisation using neurofuzzy estimation


Roberts, J.M., Mills, D.J., Charnley, D. and Harris, C.J. (1995) Improved kalman filter initialisation using neurofuzzy estimation. 4th Int. Conf. on Artificial Neural Networks , 329--334.

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Description/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. When a filter diverges, it must be re-initialised but because the observations are extremely poor, re-initialised states will have poor estimates. This paper will propose 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Organisation: IEE Address: Cambridge, U.K.
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 250141
Date Deposited: 04 May 1999
Last Modified: 27 Mar 2014 19:51
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/250141

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