Federated Adaptive Kalman Filtering and its application

In order to deal with the problem in which the Federated Kalman Filtering (FKF) may be instable or divergent when noise statistics is unknown, a new federated filtering is presented, which is defined as Federated Adaptive Kalman Filtering (FAKF). A factor of modified the measurement noise covariance...

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Bibliographic Details
Published in2008 7th World Congress on Intelligent Control and Automation pp. 1369 - 1372
Main Author Long Zhao
Format Conference Proceeding
LanguageChinese
English
Published IEEE 01.06.2008
Subjects
adaptive filtering
federated filtering
Filtering
Global positioning system
integrated navigation system
Kalman filters
Navigation
Nickel
Noise
Satellite navigation systems
satellite positioning system
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ISBN1424421136
9781424421138
DOI10.1109/WCICA.2008.4593122

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Summary:In order to deal with the problem in which the Federated Kalman Filtering (FKF) may be instable or divergent when noise statistics is unknown, a new federated filtering is presented, which is defined as Federated Adaptive Kalman Filtering (FAKF). A factor of modified the measurement noise covariance was built by using the ratio between filter residual and actual residual in FAKF. The adaptive estimation of FKF was realized by online modifying the measurement noise covariance. FAKF and FKF were compared using practical measuring data in inertial navigation system/global positioning system/double-star system (INS/GPS/DS) integrated navigation system. Simulation results show that FAKF has adaptability and has better estimation accuracy than the FKF when noise statistics information is unknown.
ISBN:1424421136
9781424421138
DOI:10.1109/WCICA.2008.4593122