Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises
This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are different. The noises of different sensors are cross-correlated and coupled with the system noise of the previous step. By use of the projection theory and induction hypothesis repeatedly, a sequential fusion estimation algorithm is derived. The algorithm is proven to be optimal in the sense of Linear Minimum Mean Square Error(LMMSE). Finally, a numerical example is presented to illustrate the effectiveness of the proposed algorithm.
KeywordsState estimation Data fusion Cross-correlated noises Asynchronous multirate multisensor
The corresponding author of this article is Liping Yan, whose work was supported by the NSFC under grants 61004139 and 91120003, the Scientific research base support, and the outstanding youth foundation of Beijing Institute of Technology. The work of Yuanqing Xia and Mengyin Fu was supported by the NSFC under grants 60974011 and 60904086, respectively. The work of Bo Xiao was supported by Beijing Natural Science Foundation under Grant 4123102, and the innovation youth foundation of Beijing University of Posts and Telecommunications.
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