| MULTISENSOR DISTRIBUTED EXTENDED KALMAN FILTERING ALGORITHM AND ITS APPLICATION TO RADAR/IR TARGET TRACKING |
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| 作者姓名： | Cui Ningzhou Xie Weixin Yu Xiongnan Ma Yuanliang |
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| 作者单位： | Cui Ningzhou Xie Weixin Yu Xiongnan Ma Yuanliang
(Marine Engineering College，Northwestern Polytechnical University，Xi'an 710072) (Electronic Engineering College，Xidian University，Xi'an 710071) |
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| 摘 要： | A multisensor distributed extended Kalman filtering algorithm is presented for nonlinear system, in which the dynamic equation of the system and the equations of sensor's measurements are linearized in the global estimate and global prediction respectively and the suboptimal global estimate based on all available information can be reconstructed from the estimates computed by local sensors based solely on their own local information and transmitted to the data fusion center. An analysis of the properties of the algorithm presented here shows that the global estimate has higher precision than the local one and smaller linearization error than the existing method. Finally, an application of the algorithm to radar/IR tracking of a maneuvering target is illustrated. Simulation results show the effectiveness of the algorithm.
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Multisensor distributed extended Kalman filtering algorithm and its application to radar/IR target tracking |
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| Cui Ningzhou Xie Weixin Yu Xiongnan Ma Yuanliang.MULTISENSOR DISTRIBUTED EXTENDED KALMAN FILTERING ALGORITHM AND ITS APPLICATION TO RADAR/IR TARGET TRACKING[J].Journal of Electronics,1998,15(1):69-75. |
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| Authors: | Ningzhou Cui Weixin Xie Xiongnan Yu Yuanliang Ma |
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| Affiliation: | (1) Marine Engineering College, Northwestern Polytechnical University, 710072 Xi’an;(2) Electronic Engineering College, Xidian University, 710071 Xi’an |
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| Abstract: | A multisensor distributed extended Kalman filtering algorithm is presented for nonlinear system, in which the dynamic equation of the system and the equations of sensor's measurements are linearized in the global estimate and global prediction respectively and the suboptimal global estimate based on all available information can be reconstructed from the estimates computed by local sensors based solely on their own local information and transmitted to the data fusion center. An analysis of the properties of the algorithm presented here shows that the global estimate has higher precision than the local one and smaller linearization error than the existing method. Finally, an application of the algorithm to radar/IR tracking of a maneuvering target is illustrated. Simulation results show the effectiveness of the algorithm. |
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| Keywords: | Extended Kalman filtering Target tracking Distributed estimation Data fusion |
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