Abstract
For multi-sensor systems with uncertainties of noise variances, a local robust steady-state Kalman filter with conservative upper bounds of unknown noise variances is presented. Based on the Lyapunov equation, its robustness is proved. Further, the covariance intersection (CI) fusion robust steady-state Kalman filter is presented. It is proved that its robust accuracy is higher than that of each local robust Kalman filter. 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., Feng, W., Deng, Z. (2013). Covariance Intersection Fusion Robust Steady-State Kalman Filter for Multi-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_95
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DOI: https://doi.org/10.1007/978-3-642-38524-7_95
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