Abstract
In order to overcome the drawbacks of the fault detection method based on \( \chi^{2} \) test that is insensitive to soft fault detection, an adaptive dynamic robust Kalman based on variance inflation model was developed, which can detect the soft fault of system. The proposed method cumulates the residuals in open windows. When the cumulant surpasses the threshold, the error covariance is enlarged to prevent abnormal Global Positioning System (GPS) observations. This method has been applied to integrated navigation system of Inertial Navigation System/Global Navigation Satellite System (INS/GNSS). The simulation results show that the soft fault is detected by using adaptive dynamic robust Kalman, and the filtering precision is higher than the traditional Kalman filtering algorithm.
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Acknowledgments
Project supported by the key program of the National Natural Science Foundation of China (Grant No. 61039003), the National Natural Science Foundation of China (Grant No. 41274038), the Aeronautical Science Foundation of China (Grant No. 20100851018)and the Aerospace Innovation Foundation of China(Grant No. CASC201102).
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Zhao, L., Yan, H. (2013). An Adaptive Dynamic Kalman Filtering Algorithm Based on Cumulative Sums of Residuals. In: Sun, J., Jiao, W., Wu, H., Shi, C. (eds) China Satellite Navigation Conference (CSNC) 2013 Proceedings. Lecture Notes in Electrical Engineering, vol 245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37407-4_67
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DOI: https://doi.org/10.1007/978-3-642-37407-4_67
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