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.
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