Abstract
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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References
Arasaratnam I (2009). Cubature Kalman filtering: theory & applications. PhD thesis, Department of Electrical & Computer Eng., McMaster University.
Arasaratnam I, Haykin S (2009). Cubature Kalman filter. IEEE Trans. Automatic Cont., 54, 1254–1269.
Bae JH, Kim YD (2010). Attitude estimation for satellite fault tolerant system using federated unscented Kalman filter. International Journal of Aeronautical and Space Sciences, 11(2), 80–86.
Ding Weidong, Wang Jinling, Chris R (2007). Improving adaptive Kalman estimation in GPS/INS integration. Journal of Navigation, 60(3), 517–529.
Francois C, Emmanuel D, Denis P, Philippe V (2006). GPS/IMU data fusion using multisensor Kalman filtering introduction of contextual aspects. Information Fusion, 7(2), 221–230.
Julien M, Hélène PL, Frédéric D, Eric W (2009). Autonomous fault diagnosis: state of the art and aeronautical benchmark. Proceedings of the 3rd European Conference for Aero-Space Sciences (EUCASS’2009), Versailles, 1–10.
Jwo DJ, Lai SY (2009). Navigation integration using the fuzzy strong tracking unscented Kalman filter. Journal of Navigation, 62(2), 303–322.
Jwo DJ, Wang SH (2007). Fuzzy adaptive strong tracking extended Kalman filtering for GPS navigation. IEEE Sensors Journal, 7(5), 778–789.
Karlgaard CD, Schaub H (2008). Adaptive huber-based filtering using projection statistics application to spacecraft attitude estimation. AIAA Guidance, Navigation and Control Conference, Honolulu, Hawaii, 1–21.
Qian Huaming, An Di, Xia Quanxi (2010). IMM-UKF based land-vehicle navigation with low-cost GPS/INS. Proceedings of the 2010 IEEE International Conference on Information and Automation, Harbin, 2031–2035.
Söken HE, Hajiyev C (2009). Adaptive unscented Kalman filter with multiple fading factors for pico satellite attitude estimation. 4th International Conference on Recent Advances in Space Technologies, Istanbul, Turkey, 541–546.
Söken HE, Hajiyev C (2010). Pico satellite attitude estimation via robust unscented Kalman filter in the presence of measurement faults. ISA Transactions, 49(3), 249–256.
Tang Lei, Zhao Chunxia, Tang Zhenmin, Cheng Weiming, Zhang Haofeng (2007). GPS/DR data fusion based on fuzzy adaptive Kalman filter. Control Theory & Applications, 24(6), 891–895.
Tseng CH, Chang CW, Jwo DJ (2011). Fuzzy adaptive multiple model nonlinear filter for integrated navigation sensor fusion. Sensors, 11(2), 2090–2111.
Wang Guangding, Zhang Shengkang, Yang Ruliang (2008). An adaptive outlier algorithm based on Kalman filtering for Beidou satellite passive combination navigation. Journal of Electronics & Information Technology, 30(8), 1981–1984.
Xia Bing, Cai Tijin (2009). Fault diagnosis and tolerant algorithms in gravity aided inertial navigation system. Journal of Chinese Inertial Technology, 17(1), 102–106.
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Foundation item: Supported by the National Natural Science Foundation of China (50979017, NSFC60775060), and the National High Technology Ship Research Project of China (GJCB09001).
Xiaogong Lin was born in 1962. He is a professor and a PhD supervisor at Harbin Engineering University. His current research interests include detection technology and automation device.
Shusheng Xu was born in 1966. He is a PhD candidate at Harbin Engineering University. His current research interest is detection technology and automation device.
Yehai Xie was born in 1986. He is a PhD candidate at Harbin Engineering University. His current research interest is detection technology and automation device.
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Lin, X., Xu, S. & Xie, Y. Multi-sensor hybrid fusion algorithm based on adaptive square-root cubature Kalman filter. J. Marine. Sci. Appl. 12, 106–111 (2013). https://doi.org/10.1007/s11804-013-1164-y
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DOI: https://doi.org/10.1007/s11804-013-1164-y