Liu Guoliang, Tian Guohui
School of Control Science and Engineering, Shandong University, Jinan 250061, China.
Sensors (Basel). 2017 Apr 8;17(4):800. doi: 10.3390/s17040800.
This paper focuses on the convergence rate and numerical characteristics of the nonlinear information consensus filter for object tracking using a distributed sensor network. To avoid the Jacobian calculation, improve the numerical characteristic and achieve more accurate estimation results for nonlinear distributed estimation, we introduce square-root extensions of derivative-free information weighted consensus filters (IWCFs), which employ square-root versions of unscented transform, Stirling's interpolation and cubature rules to linearize nonlinear models, respectively. In addition, to improve the convergence rate, we introduce the square-root dynamic hybrid consensus filters (DHCFs), which use an estimated factor to weight the information contributions and shows a faster convergence rate when the number of consensus iterations is limited. Finally, compared to the state of the art, the simulation shows that the proposed methods can improve the estimation results in the scenario of distributed camera networks.
本文聚焦于使用分布式传感器网络进行目标跟踪的非线性信息共识滤波器的收敛速度和数值特性。为避免雅可比矩阵计算、改善数值特性并在非线性分布式估计中获得更精确的估计结果，我们引入了无导数信息加权共识滤波器（IWCF）的平方根扩展，其分别采用无迹变换、斯特林插值和容积规则的平方根版本来线性化非线性模型。此外，为提高收敛速度，我们引入了平方根动态混合共识滤波器（DHCF），其使用一个估计因子对信息贡献进行加权，并且在共识迭代次数有限时显示出更快的收敛速度。最后，与现有技术相比，仿真表明所提出的方法能够在分布式相机网络场景中改善估计结果。
