In fuzzy Kalman filtering, each local Kalman filter makes use of its own model about the system’s dynamics and the local state estimates are fused into one single state estimate through fuzzy weighting. These models can be inaccurate or the system’s dynamics may change. The objective of the paper is to provide a method for finding out which one of the models used by the local Kalman filters contains parameters that deviate from the nominal values of the parameters of the real system. This is a problem of validation of the fuzzy Kalman filter, that is solved with the use of the local statistical approach to fault diagnosis. The development of a tool which can detect incipient changes in the parameters of the fuzzy Kalman filter is important, since such types of distributed Kalman filters are used in numerous applications that need precision and reliability in the provided state estimation (sensor networks, navigation systems, industrial production systems etc.).

Fuzzy Kalman Filter Validation Using the Local Statistical Approach

SIANO, PIERLUIGI
2015-01-01

Abstract

In fuzzy Kalman filtering, each local Kalman filter makes use of its own model about the system’s dynamics and the local state estimates are fused into one single state estimate through fuzzy weighting. These models can be inaccurate or the system’s dynamics may change. The objective of the paper is to provide a method for finding out which one of the models used by the local Kalman filters contains parameters that deviate from the nominal values of the parameters of the real system. This is a problem of validation of the fuzzy Kalman filter, that is solved with the use of the local statistical approach to fault diagnosis. The development of a tool which can detect incipient changes in the parameters of the fuzzy Kalman filter is important, since such types of distributed Kalman filters are used in numerous applications that need precision and reliability in the provided state estimation (sensor networks, navigation systems, industrial production systems etc.).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4656504
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