School of Automation, Northwestern Polytechnical University, Xi'an, 710129, China; Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an, 710129, China.
ISA Trans. 2023 Jun;137:531-543. doi: 10.1016/j.isatra.2022.12.016. Epub 2022 Dec 29.
This paper proposes a fault-tolerant distributed Bayesian filter for multi-sensor state estimation using a peer-to-peer sensor network with incoherent local estimates problems. The proposed approach uses a Gaussian mixture rather than a single Gaussian distribution to represent the fusion result, which can effectively reduce the negative impact of corrupted local estimates on the fusion results. The resulting filter performs Bayesian recursion via Gaussian mixture. To accommodate a heterogeneous sensor network, we develop a novel arithmetic average fusion employing a set of covariance-dependent weighting coefficients, where the fusion error covariance is effectively reduced in the case of fusing information with different qualities. For intersensor communication, a partial flooding scheme is investigated, in which only valid-likely Gaussian components are disseminated and fused between neighbor sensors. Theoretically, it is shown that under reasonable assumptions, the presented fault-tolerant distributed estimator can guarantee local stability with the exponentially bounded estimation error in the mean square. The effectiveness and superiority of our approach are validated through both simulation and experiment scenarios.
本文提出了一种基于对等传感器网络的多传感器状态容错分布式贝叶斯滤波器，解决了局部估计不一致的问题。所提出的方法使用高斯混合而不是单个高斯分布来表示融合结果，这可以有效地减少损坏的局部估计对融合结果的负面影响。所得到的滤波器通过高斯混合执行贝叶斯递归。为了适应异构传感器网络，我们开发了一种新颖的算术平均融合方法，采用了一组协方差相关的加权系数，在融合质量不同的信息时，有效地降低了融合误差协方差。对于传感器间通信，研究了一种部分泛洪方案，其中仅在邻居传感器之间传播和融合有效似然高斯分量。理论上，在合理假设下，所提出的容错分布式估计器可以保证局部稳定性，并在均方意义上具有指数有界的估计误差。通过仿真和实验场景验证了我们方法的有效性和优越性。
