| Hu Zhentao (胡振涛)*,Fu Chunling**,Li Junwei*.[J].高技术通讯(英文),2015,21(3):301~308 |
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| Probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update |
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| DOI：10.3772/j.issn.1006-6748.2015.03.009 |
| 中文关键词: |
| 英文关键词: nonlinear filter, observation iterated update, ensemble Kalman filter (EnKF), probabilistic data association (PDA) |
| 基金项目: |
| Author Name | Affiliation | | Hu Zhentao (胡振涛)* | | | Fu Chunling** | | | Li Junwei* | |
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| 中文摘要: |
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| 英文摘要: |
| Aiming at improving the observation uncertainty caused by limited accuracy of sensors, and the uncertainty of observation source in clutters, through the dynamic combination of ensemble Kalman filter(EnKF) and probabilistic data association(PDA), a novel probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update is proposed. Firstly, combining with the advantages of data assimilation handling observation uncertainty in EnKF, an observation iterated update strategy is used to realize optimization of EnKF in structure. And the object is to further improve state estimation precision of nonlinear system. Secondly, the above algorithm is introduced to the framework of PDA, and the object is to increase reliability and stability of candidate echo acknowledgement. In addition, in order to decrease computation complexity in the combination of improved EnKF and PDA, the maximum observation iterated update mechanism is applied to the iteration of PDA. Finally, simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters. |
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