Fully distributed variational Bayesian non-linear filter with unknown measurement noise in sensor networks
Fully distributed variational Bayesian non-linear filter with unknown measurement noise in sensor networks作者机构：Research Institute of Information Fusion Naval Aviation University Department of Electronic Engineering Tsinghua University
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2020年第63卷第11期
页 面：24-37页
核心收录：
学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 080202[工学-机械电子工程] 08[工学] 0802[工学-机械工程]
基 金：supported by National Natural Science Foundation of China (Grant Nos. 61790550 91538201 61531020 61671463)
主 题：sensor networks distributed state estimation cubature Kalman filter variational Bayesian inference Kullback-Leibler divergence
摘 要：In practical applications, the measurement noise statistics is usually unknown or may change over time. However, most existing distributed filtering algorithms for sensor networks are constructed based on exact knowledge of measurement noise statistics. Therefore, under situations with measurement uncertainty, the existing algorithms may result in deteriorated performance. To solve such problems, a distributed adaptive cubature information filter based on variational Bayesian(VB-DACIF) is proposed here. Firstly,the predicted estimates of interest from inclusive neighbours are fused by minimizing the weighted KullbackLeibler average, in which the cubature rule is utilized to tackle system nonlinearity. Then, the free form variational Bayesian approximation is applied to recursively update both the local estimate and the precision matrices of sensing nodes. Finally, the posterior Cram′er-Rao lower bound is exploited to evaluate performance of the proposed VB-DACIF. Simulation results with a maneuvering target tracking scenario validates the feasibility and superiority of the proposed VB-DACIF.
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