Yan Liping, Di Chenying, Wu Q M Jonathan, Xia Yuanqing, Liu Shida
Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Department of Electrical and Computer Engineering, University of Windsor, Windsor N9B3P4, Canada.
Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China.
ISA Trans. 2020 Jun;101:160-169. doi: 10.1016/j.isatra.2020.02.004. Epub 2020 Feb 13.
Student's t distribution is a useful tool that can model heavy-tailed noises appearing in many practical systems. Although t distribution based filter has been derived, the information filter form is not presented and the data fusion algorithms for dynamic systems disturbed by heavy-tailed noises are rarely concerned. In this paper, based on multivariate t distribution and variational Bayesian estimation, the information filter, the centralized batch fusion, the distributed fusion, and the suboptimal distributed fusion algorithms are derived, respectively. The centralized fusion is given in two forms, namely, from t distribution based filter and the proposed t distribution based information filter, respectively. The distributed fusion is deduced by the use of the newly derived information filter, and it has been demonstrated to be equivalent to the centralized batch fusion. The suboptimal distributed fusion is obtained by a parameter approximation from the derived distributed fusion to decrease the computation complexity. The presented algorithms are shown to be the generalization of the classical Kalman filter based traditional algorithms. Theoretical analysis and exhaustive experimental analysis by a target tracking example show that the proposed algorithms are feasible and effective.
学生t分布是一种有用的工具，可对许多实际系统中出现的重尾噪声进行建模。尽管基于t分布的滤波器已经推导出来，但信息滤波器形式尚未给出，且很少有人关注受重尾噪声干扰的动态系统的数据融合算法。本文基于多元t分布和变分贝叶斯估计，分别推导了信息滤波器、集中式批处理融合、分布式融合和次优分布式融合算法。集中式融合以两种形式给出，即分别基于基于t分布的滤波器和所提出的基于t分布的信息滤波器。分布式融合通过使用新推导的信息滤波器得出，并已证明其与集中式批处理融合等效。次优分布式融合通过对推导的分布式融合进行参数近似得到，以降低计算复杂度。所提出的算法被证明是基于经典卡尔曼滤波器的传统算法的推广。通过一个目标跟踪示例进行的理论分析和详尽的实验分析表明，所提出的算法是可行且有效的。
