Comparison of three fuzzy logic-based adaptive multisensor data fusion architectures

P. J. Escamilla-Ambrosio; N. Mort

doi:10.1016/S1474-6670(17)31088-1

Comparison of three fuzzy logic-based adaptive multisensor data fusion architectures

P. J. Escamilla-Ambrosio, N. Mort

Research output: Contribution to journal › Conference article › peer-review

5 Scopus citations

Abstract

In this work the fault-tolerant performance of the recently developed fuzzy logic-based adaptive centralised, decentralised, and federated Kalman filters are compared when used for multisensor data fusion purposes. The adaptation is in the sense of adaptively tuning the measurement noise covariance matrix to fit the actual statistics of the noise profiles present in the incoming measured data. A fuzzy inference system based on a covariance-matching technique is used as the adaptation mechanism. The fault-tolerant performance of the three approaches is compared through result from several simulated tests.

Original language	English
Pages (from-to)	103-108
Number of pages	6
Journal	IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume	37
Issue number	14
DOIs	https://doi.org/10.1016/S1474-6670(17)31088-1
State	Published - 2004
Externally published	Yes
Event	3rd IFAC Symposium on Mechatronic Systems 2004 - Sydney, Australia Duration: 6 Sep 2004 → 8 Sep 2004

Keywords

Adaptive Kalman filters
Fuzzy inference systems
Multisensor data fusion

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10.1016/S1474-6670(17)31088-1

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abstract = "In this work the fault-tolerant performance of the recently developed fuzzy logic-based adaptive centralised, decentralised, and federated Kalman filters are compared when used for multisensor data fusion purposes. The adaptation is in the sense of adaptively tuning the measurement noise covariance matrix to fit the actual statistics of the noise profiles present in the incoming measured data. A fuzzy inference system based on a covariance-matching technique is used as the adaptation mechanism. The fault-tolerant performance of the three approaches is compared through result from several simulated tests.",

keywords = "Adaptive Kalman filters, Fuzzy inference systems, Multisensor data fusion",

author = "Escamilla-Ambrosio, {P. J.} and N. Mort",

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AU - Mort, N.

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AB - In this work the fault-tolerant performance of the recently developed fuzzy logic-based adaptive centralised, decentralised, and federated Kalman filters are compared when used for multisensor data fusion purposes. The adaptation is in the sense of adaptively tuning the measurement noise covariance matrix to fit the actual statistics of the noise profiles present in the incoming measured data. A fuzzy inference system based on a covariance-matching technique is used as the adaptation mechanism. The fault-tolerant performance of the three approaches is compared through result from several simulated tests.

KW - Adaptive Kalman filters

KW - Fuzzy inference systems

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JO - IFAC Proceedings Volumes (IFAC-PapersOnline)

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T2 - 3rd IFAC Symposium on Mechatronic Systems 2004

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ER -

Comparison of three fuzzy logic-based adaptive multisensor data fusion architectures

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Keywords

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