Abstract
For two-sensor systems with time-delayed measurements and uncertain noise variances, this paper presents a measurements transformation approach which transforms the systems with time-delayed measurements into the equivalent systems without measurement delays. Further the local robust steady-state Kalman filter with conservative upper bounds of unknown noise variances is presented, and then the covariance intersection (CI) fusion robust steady-state Kalman filter is also presented. The robustness of these filters is proved based on the Lyapunov equation. It is proved that the robust accuracy of the CI fuser 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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© 2013 Springer-Verlag Berlin Heidelberg
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Qi, W., Zhang, P., Feng, W., Deng, Z. (2013). Covariance Intersection Fusion Robust Steady-State Kalman Filter for Two-Sensor Systems with Time-Delayed Measurements. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38460-8_24
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DOI: https://doi.org/10.1007/978-3-642-38460-8_24
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