| 基于自适应无迹粒子滤波的目标跟踪算法 |
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| 引用本文： | 李昱辰,李战明.基于自适应无迹粒子滤波的目标跟踪算法[J].光电子．激光,2012(10):1983-1989. |
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| 作者姓名： | 李昱辰 李战明 |
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| 作者单位： | 兰州理工大学电气工程与信息工程学院甘肃省工业过程先进控制重点实验室;兰州理工大学电气工程与信息工程学院甘肃省工业过程先进控制重点实验室 |
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| 基金项目： | 国家自然科学基金(60964003);教育部博士点基金(20106201110003)资助项目 |
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| 摘 要： | 为解决复杂场景中目标跟踪问题,提出了一种噪声未知情况下的自适应无迹粒子滤波(A-UPF)算法。算法采用改进的Sage-Husa估计器对系统未知噪声的统计特性进行实时估计和修正,并与无迹Kalman粒子滤波器相结合产生优选的建议分布函数,降低系统估计误差的同时有效提升了系统的抗噪声能力。实验结果表明,本文方法对于复杂条件下的目标跟踪问题具有较高的精度和较强的鲁棒性。
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| 关 键 词： | 目标跟踪 粒子滤波 自适应滤波 无迹Kalman滤波 |
Object tracking algorithm based on adaptive unscented particle filter |
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| LI Zhan-ming.Object tracking algorithm based on adaptive unscented particle filter[J].Journal of Optoelectronics·laser,2012(10):1983-1989. |
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| Authors: | LI Zhan-ming |
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| Affiliation: | Key Laboratory of Advanced Control of Industrial Processes of Gansu Province,College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Key Laboratory of Advanced Control of Industrial Processes of Gansu Province,College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China |
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| Abstract: | In order to sovle the problem of target tracking under the complex scenes,this paper proposes an adaptive unscented particle filter(A-UPF) algorithm for the system with unknown noise.The new algorithm estimates and corrects the statistic characteristics of the system unknown noise in real-time by improved Sage-Husa estimator,and produces optimal distribution function with unscented Kalman filter.The new algorithm reduces the estimation error effectively and improves the anti-noise ability of the system.The experimental results show that the method proposed in this paper has high precision and strong robustness for target tracking under the complicated conditions. |
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| Keywords: | object tracking particle filter adaptive filter unscented Kalman filter |
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