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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 At 4th Int. Conf. on Artificial Neural Networks. , 329--334.

Record type: Conference or Workshop Item (Other)


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.

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Published date: 1995
Additional Information: Organisation: IEE Address: Cambridge, U.K.
Venue - Dates: 4th Int. Conf. on Artificial Neural Networks, 1995-01-01
Organisations: Southampton Wireless Group


Local EPrints ID: 250141
PURE UUID: d98b5e01-3763-40b9-84cb-d766edaa5cf1

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Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:43

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Author: J.M. Roberts
Author: D.J. Mills
Author: D. Charnley
Author: C.J. Harris

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