| State Fusion Estimation for Multilevel Multisensor System |
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| 作者姓名： | 金学波 孙优贤 |
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| 作者单位： | Jin Xuebo & Sun Youxian
1. National Laboratory of Industrial Control Technology，Zhejiang University，Hangzhou 310027，P. R. China;
2. College of Informatics and Electronics，Zhejiang Institute of Science and Technology，Hangzhou 310033，P. R. China |
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| 摘 要： | Based on the single sensor Kalman filtering equations, this paper presents two-level and three-level optimal centralized and distributed estimation algorithms for hierarchical multisensor systems. The solution shows that when the correlated matrix, the mean of noise, the control input, and the measurement error are all zero, the result in this paper turns out to be the standard algorithm discussed. Simulation shows that the mean of noise, the control input, and the measurement error will not change the estimation covariance and the estimation covariance fluctuates greatly when the cross-correlated matrix is similar to the covariance of process noise.
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State Fusion Estimation for Multilevel Multisensor System |
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| Jin Xuebo & Sun Youxian
. National Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou ,P. R. China,
. College of Informatics and Electronics,Zhejiang Institute of Science and Technology,Hangzhou ,P. R. China.State Fusion Estimation for Multilevel Multisensor System[J].Journal of Systems Engineering and Electronics,2003,14(4). |
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| Authors: | Jin Xuebo & Sun Youxian
National Laboratory of Industrial Control Technology Zhejiang University Hangzhou P R China College of Informatics and Electronics Zhejiang Institute of Science and Technology Hangzhou P R China |
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| Institution: | 1. National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, P. R. China;College of Informatics and Electronics, Zhejiang Institute of Science and Technology,Hangzhou 310033, P. R. China
2. National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, P. R. China |
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| Abstract: | Based on the single sensor Kalman filtering equations, this paper presents two-level and three-level optimal centralized and distributed estimation algorithms for hierarchical multisensor systems. The solution shows that when the correlated matrix, the mean of noise, the control input, and the measurement error are all zero, the result in this paper turns out to be the standard algorithm discussed. Simulation shows that the mean of noise, the control input, and the measurement error will not change the estimation covariance and the estimation covariance fluctuates greatly when the cross-correlated matrix is similar to the covariance of process noise. |
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| Keywords: | data fusion hierarchical estimation multilevel filtering correlated noise |
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