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Sensor validation and fusion using the Nadaraya-Watson statistical estimator

Sensor validation and fusion using the Nadaraya-Watson statistical estimator
Sensor validation and fusion using the Nadaraya-Watson statistical estimator
The paper describes a novel integrated sensor validation and fusion scheme based on the Nadaraya-Watson statistical estimator. The basis of the sensor validation scheme is that observations used to implement the estimator are obtained from valid sensor readings. Pattern matching techniques are used to relate a measurement vector that is not consistent with the training data to the closest a-priori observation. Defective sensor(s) can be identified and 'masked', and the estimator reconfigured to compute the estimate using data from the remaining sensors. Test results are provided for a range of typical fault conditions using an array of thick film pH sensors. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The fused result is more accurate than the single best sensor.
0972184414
321-326
Institution of Electronic and Electrical Engineers
Wellington, S.J.
d65b1a0c-1b43-443d-99fd-9a4daebe0822
Atkinson, J.K.
5e9729b2-0e1f-400d-a889-c74f6390ea58
Sion, R.P.
b702a661-c816-44c3-8131-c9bb064d282c
Wellington, S.J.
d65b1a0c-1b43-443d-99fd-9a4daebe0822
Atkinson, J.K.
5e9729b2-0e1f-400d-a889-c74f6390ea58
Sion, R.P.
b702a661-c816-44c3-8131-c9bb064d282c

Wellington, S.J., Atkinson, J.K. and Sion, R.P. (2002) Sensor validation and fusion using the Nadaraya-Watson statistical estimator. In Proceedings of the Fifth International Conference on Information Fusion. Institution of Electronic and Electrical Engineers. pp. 321-326 . (doi:10.1109/ICIF.2002.1021169).

Record type: Conference or Workshop Item (Paper)

Abstract

The paper describes a novel integrated sensor validation and fusion scheme based on the Nadaraya-Watson statistical estimator. The basis of the sensor validation scheme is that observations used to implement the estimator are obtained from valid sensor readings. Pattern matching techniques are used to relate a measurement vector that is not consistent with the training data to the closest a-priori observation. Defective sensor(s) can be identified and 'masked', and the estimator reconfigured to compute the estimate using data from the remaining sensors. Test results are provided for a range of typical fault conditions using an array of thick film pH sensors. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The fused result is more accurate than the single best sensor.

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More information

Published date: 2002
Venue - Dates: Fifth International Conference on Information Fusion (FUSION 2002), 2002-07-08 - 2002-07-11

Identifiers

Local EPrints ID: 22591
URI: http://eprints.soton.ac.uk/id/eprint/22591
ISBN: 0972184414
PURE UUID: d2650cd2-b9a0-4b5e-bfbf-a5c35a05f19e
ORCID for J.K. Atkinson: ORCID iD orcid.org/0000-0003-3411-8034

Catalogue record

Date deposited: 08 Mar 2007
Last modified: 29 Aug 2019 00:56

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Contributors

Author: S.J. Wellington
Author: J.K. Atkinson ORCID iD
Author: R.P. Sion

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