Distributed fusion receding horizon filtering for uncertain linear stochastic systems with time-delay sensors |
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| Authors: | Il Young Song Vladimir Shin Moongu Jeon |
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| Institution: | 1. School of Information and Communications, Gwangju Institute of Science and Technology, 1 Oryong-Dong Buk-Gu, Gwangju 500-712, South Korea;2. Department of Information and Statistics, Gyeonsang National University, 501 Jinjudaero, Jinju City, Gyeongsangnam-do 660-701, South Korea;1. Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, PR China;2. Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;1. School of Electrical Engineering, Korea University, Seoul, Republic of Korea;2. College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China;3. College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia;4. Department of Electronics Convergence Engineering, Wonkwang University, Iksan, Republic of Korea;1. Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin 150080, PR China;2. Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. Liaoning Province Key Laboratory of Control Technology for Chemical Processes, Shenyang University of Chemical Technology, Shenyang 110142, China;2. Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada |
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| Abstract: | A new distributed fusion receding horizon filtering problem is investigated for uncertain linear stochastic systems with time-delay sensors. First, we construct a local receding horizon Kalman filter having time delays (LRHKFTDs) in both the system and measurement models. The key technique is the derivation of recursive error cross-covariance equations between LRHKFTDs in order to compute the optimal matrix fusion weights. It is the first time to present distributed fusion receding horizon filter for linear discrete-time systems with delayed sensors. It has a parallel structure that enables processing of multisensory time-delay measurements, so the calculation burden can be reduced and it is more reliable than the centralized version if some sensors turn faulty. Simulations for a multiple time-delays system show the effectiveness of the proposed filter in comparison with centralized receding horizon filter and non-receding versions. |
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