Abstract
For two-sensor systems with uncertainties of noise variances, a local robust steady-state Kalman one-step and multi-step predictors with the minimum upper bounds variances are presented respectively. Their robustness is proved based on the Lyapunov equation. Further, the covariance intersection (CI) fusion robust steady-state Kalman predictors are also presented by the convex combination of the local robust Kalman predictors. It is proved that its robust accuracy is higher than that of each local robust Kalman predictor. A Monte-Carlo simulation example shows its correctness and effectiveness.
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Acknowledgments
This work is supported by the Natural Science Foundation of China under grant NSFC-60874063, the 2012 Innovation and Scientific Research Foundation of graduate student of Heilongjiang Province under grant YJSCX2012-263HLJ, and the Support Program for Young Professionals in Regular Higher Education Institutions of Heilongjiang Province under grant 1251G012.
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Qi, W., Zhang, P., Deng, Z. (2013). Covariance Intersection Fusion Robust Steady-State Kalman Predictor for Two-Sensor Systems with Unknown Noise Variances. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38524-7_91
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DOI: https://doi.org/10.1007/978-3-642-38524-7_91
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