Hu Chen, Lin Haoshen, Li Zhenhua, He Bing, Liu Gang
Xi'an Institute of High-Tech, Xi'an 710025, Shaanxi, China.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China.
Entropy (Basel). 2018 Feb 10;20(2):116. doi: 10.3390/e20020116.
In this paper, a distributed Bayesian filter design was studied for nonlinear dynamics and measurement mapping based on Kullback-Leibler divergence. In a distributed structure, the nonlinear filter becomes a challenging problem, since each sensor cannot access the global measurement likelihood function over the whole network, and some sensors have weak observability of the state. To solve the problem in a sensor network, the distributed Bayesian filter problem was converted into an optimization problem by maximizing a posterior method. The global cost function over the whole network was decomposed into the sum of the local cost function, where the local cost function can be solved by each sensor. With the help of the Kullback-Leibler divergence, the global estimate was approximated in each sensor by communicating with its neighbors. Based on the proposed distributed Bayesian filter structure, a distributed cubature Kalman filter (DCKF) was proposed. Finally, a cooperative space object tracking problem was studied for illustration. The simulation results demonstrated that the proposed algorithm can solve the issues of varying communication topology and weak observability of some sensors.
本文研究了基于库尔贝克-莱布勒散度的非线性动力学和测量映射的分布式贝叶斯滤波器设计。在分布式结构中，非线性滤波器成为一个具有挑战性的问题，因为每个传感器无法获取整个网络的全局测量似然函数，并且一些传感器对状态的可观测性较弱。为了解决传感器网络中的这个问题，通过最大后验方法将分布式贝叶斯滤波器问题转化为一个优化问题。整个网络的全局代价函数被分解为局部代价函数之和，其中局部代价函数可由每个传感器求解。借助库尔贝克-莱布勒散度，通过与邻居通信在每个传感器中近似全局估计。基于所提出的分布式贝叶斯滤波器结构，提出了一种分布式容积卡尔曼滤波器（DCKF）。最后，为了进行说明，研究了一个协同空间目标跟踪问题。仿真结果表明，所提出的算法能够解决通信拓扑变化和一些传感器可观测性较弱的问题。
