Li Jiahong, Deng Fang, Chen Jie
IEEE Trans Cybern. 2019 Jul;49(7):2431-2443. doi: 10.1109/TCYB.2018.2815697. Epub 2018 Mar 27.
For multisensor linear time-varying system with non-Gaussian measurement noise, how to design distributed robust estimator to increase the accuracy and robustness to outliers at a relatively low computation and communication cost is a fundamental task. This paper proposes a fast distributed variational Bayesian (VB) filtering algorithm to recursively estimate the state and noise distribution over three conventional sensor networks: 1) incremental-based; 2) diffusion-based; and 3) consensus-based. To be specific, the non-Gaussian measurement noise of each sensor is modeled as Student- t distribution, and the system state and the parameters of the distribution are estimated via VB approach in each iteration step. An interaction scheme is then added to obtain the global optimal parameter by fusing the local optimal parameters over incremental, diffusion, and consensus communication topology. An efficient sensor selection criterion under these topologies based on the Cramér-Rao lower bound is proposed to reduce the communication and computation burden. Compared with the existing centralized VB filtering algorithms, the proposed algorithm in this paper can extensively increase the robustness to node or link failure at a lower computation cost with acceptable estimation performance and communication load. The theoretic results and simulation results are given to show the efficiency of our proposed algorithm.
对于具有非高斯测量噪声的多传感器线性时变系统，如何以相对较低的计算和通信成本设计分布式鲁棒估计器以提高对异常值的准确性和鲁棒性是一项基本任务。本文提出了一种快速分布式变分贝叶斯（VB）滤波算法，用于在三种传统传感器网络上递归估计状态和噪声分布：1）基于增量的；2）基于扩散的；3）基于一致性的。具体而言，将每个传感器的非高斯测量噪声建模为学生t分布，并在每个迭代步骤中通过VB方法估计系统状态和分布参数。然后添加一种交互方案，通过在增量、扩散和一致性通信拓扑上融合局部最优参数来获得全局最优参数。提出了一种基于克拉美罗下界的这些拓扑下的高效传感器选择准则，以减轻通信和计算负担。与现有的集中式VB滤波算法相比，本文提出的算法能够以较低的计算成本大幅提高对节点或链路故障的鲁棒性，同时具有可接受的估计性能和通信负载。给出了理论结果和仿真结果以证明所提算法的有效性。
