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Optimal fusion estimation for stochastic systems with cross-correlated sensor noises

  • Research Paper
  • Special Focus on Analysis and Synthesis for Stochastic Systems
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Abstract

This paper is concerned with the optimal fusion of sensors with cross-correlated sensor noises. By taking linear transformations to the measurements and the related parameters, new measurement models are established, where the sensor noises are decoupled. The centralized fusion with raw data, the centralized fusion with transformed data, and a distributed fusion estimation algorithm are introduced, which are shown to be equivalent to each other in estimation precision, and therefore are globally optimal in the sense of linear minimum mean square error (LMMSE). It is shown that the centralized fusion with transformed data needs lower communication requirements compared to the centralized fusion using raw data directly, and the distributed fusion algorithm has the best flexibility and robustness and proper communication requirements and computation complexity among the three algorithms (less communication and computation complexity compared to the existed distributed Kalman filtering fusion algorithms). An example is shown to illustrate the effectiveness of the proposed algorithms.

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Acknowledgements

The work was supported by National Natural Science Foundation of China (Grant Nos. 61225015, 61473040), Beijing Natual Science Foundation (Grant No. 4161001), and Innovative Research Groups of the National Nature Science Foundation of China (Grant No. 61321002).

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Authors and Affiliations

  1. School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China

    Liping Yan, Yuanqing Xia & Mengyin Fu

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  1. Liping Yan
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  2. Yuanqing Xia
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  3. Mengyin Fu
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Correspondence to Liping Yan.

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Yan, L., Xia, Y. & Fu, M. Optimal fusion estimation for stochastic systems with cross-correlated sensor noises. Sci. China Inf. Sci. 60, 120205 (2017). https://doi.org/10.1007/s11432-017-9140-x

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  • DOI: https://doi.org/10.1007/s11432-017-9140-x

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