The modeling and estimation of asynchronous multirate multisensor dynamic systems |
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| Institution: | 1. Department of Mechanical Engineering, University of Canterbury, Christchurch, Private Bag 4800, New Zealand (Tel: +64 3 3642987;1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;2. Department of Computer Science, Brunel University London, Middlesex, UB8 3PH, Uxbridge, United Kingdom;3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;4. School of Information Science and Technology, Donghua University, Shanghai 200051, China;1. Information Engineering University, Zhengzhou 450001, China;2. Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China;3. Rocket Force University of Engineering, Xi''an 710025, China;4. Army Logistics University of PLA, Chongqing 410331, China;1. School of Automation, Northwestern Polytechnical University, Xi’an, PR China;2. Key laboratory of Information Fusion Technology, Ministry of Education, Xi’an, PR China |
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| Abstract: | An asynchronous data fusion problem based on a kind of multirate multisensor dynamic system is studied. The system is observed by multirate sensors independently, with the state model known at the finest scale. Under the assumption that the sampling rates of sensors decrease successively by any positive integers, the discrete dynamic system models are established based on each single sensor and an asynchronous multirate multisensor state fusion estimation algorithm is presented. Theoretically, the estimate is proven to be unbiased and the optimal in the sense of linear minimum covariance, the fused estimate is better than the Kalman filtering results based on each single sensor, and the accuracy of the fused estimate will decrease if any of the sensors' information is neglected. The feasibility and effectiveness of the algorithm are shown through simulations. |
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