Self-tuning decoupled fusion Kalman filter based on the Riccati equation |
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Authors: | Xiaojun SUN Peng ZHANG Zili DENG |
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Affiliation: | (1) Department of Automation, Heilongjiang University, Harbin, 150080, China |
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Abstract: | An online noise variance estimator for multisensor systems with unknown noise variances is proposed by using the correlationmethod.
Based on the Riccati equation and optimal fusion rule weighted by scalars for state components, a self-tuning component decoupled
information fusion Kalman filter is presented. It is proved that the filter converges to the optimal fusion Kalman filter
in a realization by dynamic error system analysis method, so that it has asymptotic optimality. Its effectiveness is demonstrated
by simulation for a tracking system with 3 sensors.
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Translated from Control and Decision, 2008, 23(2): 195–199 译自: 控制与决策] |
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Keywords: | multi-sensor information fusion decoupled fusion self-tuning fuser Kalman filter convergence in a realization |
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