Abstract
For discrete time-invariant multisensors with correlated noises and different measurement matrices, a new weighted measurement fusion (WMF) Kalman estimation algorithm is presented by using the full-rank decomposition of matrix and the weighted least squares (WLS) theory. The newly presented algorithm can make the state problems of fusion filtering, smoothing, and predicating, uniformly processed, proving the estimating result equivalent to the centralized fusion (CF) Kalman estimating result. Therefore, it has a global optimality . It can obviously reduce the computational burden, and is convenient for application in real time. A simulation example shows the effectiveness of the proposed algorithm.
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Acknowledgment
The authors thank for the support from National Natural Science Foundation of P.R.China under Grant NSFC-60874062, Key Project of Chinese Ministry of Education under Grant 209038, Key Laboratory of Electronics Engineering, College of Heilongjiang Province under Grant DZZD 20100038, and Open Laboratory Project of Heilongjiang University in China under Grant 11K040. The High Level Talents Support Project of Heilongjiang University in China under Grant Hdtd2010-03 also deserves acknowledgement.
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Wang, X., Zhu, Q., Sun, S., Hao, G. (2012). Global Optimal Weighted Measurement Fusion Kalman Filter. In: Chen, R. (eds) 2011 International Conference in Electrics, Communication and Automatic Control Proceedings. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8849-2_49
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DOI: https://doi.org/10.1007/978-1-4419-8849-2_49
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