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 Xiaogong Lin,Shusheng Xu and Yehai Xie.Multi-sensor Hybrid Fusion Algorithm Based on Adaptive Square-root Cubature Kalman Filter[J].Journal of Marine Science and Application,2013,(1):106-111.[doi:10.1007/s11804-013-1164-y]
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Multi-sensor Hybrid Fusion Algorithm Based on Adaptive Square-root Cubature Kalman Filter


Multi-sensor Hybrid Fusion Algorithm Based on Adaptive Square-root Cubature Kalman Filter
Xiaogong Lin Shusheng Xu and Yehai Xie
Xiaogong Lin Shusheng Xu and Yehai Xie
College of Automation, Harbin Engineering University, Harbin 150001, China
hybrid fusion algorithm square-root cubature Kalman filter adaptive filter fault detection
In the normal operation condition, a conventional square-root cubature Kalman filter (SRCKF) gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.


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Supported by the National Natural Science Foundation of China (50979017, NSFC60775060), and the National High Technology Ship Research Project of China (GJCB09001).
Last Update: 2013-03-14