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Multisensor Estimation Fusion with Gaussian Process for Nonlinear Dynamic Systems

1
School of Mathematics, Sichuan University, Chengdu 610064, China
2
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
3
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(11), 1126; https://doi.org/10.3390/e21111126
Received: 9 October 2019 / Revised: 7 November 2019 / Accepted: 11 November 2019 / Published: 16 November 2019
(This article belongs to the Section Signal and Data Analysis)
The Gaussian process is gaining increasing importance in different areas such as signal processing, machine learning, robotics, control and aerospace and electronic systems, since it can represent unknown system functions by posterior probability. This paper investigates multisensor fusion in the setting of Gaussian process estimation for nonlinear dynamic systems. In order to overcome the difficulty caused by the unknown nonlinear system models, we associate the transition and measurement functions with the Gaussian process regression models, then the advantages of the non-parametric feature of the Gaussian process can be fully extracted for state estimation. Next, based on the Gaussian process filters, we propose two different fusion methods, centralized estimation fusion and distributed estimation fusion, to utilize the multisensor measurement information. Furthermore, the equivalence of the two proposed fusion methods is established by rigorous analysis. Finally, numerical examples for nonlinear target tracking systems demonstrate the equivalence and show that the multisensor estimation fusion performs better than the single sensor. Meanwhile, the proposed fusion methods outperform the convex combination method and the relaxed Chebyshev center covariance intersection fusion algorithm. View Full-Text
Keywords: multisensor estimation fusion; Gaussian process; nonlinear dynamic systems; data driven modeling; target tracking; information fusion multisensor estimation fusion; Gaussian process; nonlinear dynamic systems; data driven modeling; target tracking; information fusion
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Liao, Y.; Xie, J.; Wang, Z.; Shen, X. Multisensor Estimation Fusion with Gaussian Process for Nonlinear Dynamic Systems. Entropy 2019, 21, 1126.

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