传感器网络中鲁棒状态信息融合抗差卡尔曼滤波器

Anti-outlier Kalman filter-based robust estimation fusion in wireless sensor networks

中文关键词  无线传感器网络  野值  卡尔曼滤波  融合估计  相关性

英文关键词  wireless sensor network  outlier  Kalman filter  estimation fusion  correlation

中文摘要

      研究了无线传感器网络中的分布式鲁棒状态信息融合问题. 在局部状态估计层, 基于鲁棒统计学理论提出了适用于噪声相关情况的抗差(扩展)卡尔曼滤波器. 在融合中心层, 针对局部估计相关未知性和不完整性, 给出了不依赖于互协方差阵的稳健航迹融合方法—–内椭球逼近法. 仿真结果证实了算法的有效性: 所提出的抗差卡尔曼滤波器在野值存在情况下, 性能退化远低于传统卡尔曼滤波器(28.6%比428.6%); 所提出的内椭球逼近法获得比协方并交叉法更好的融合估计性能, 且不需要局部估计相关性的先验知识.

英文摘要

      The problem of distributed robust estimation fusion is considered for a hierarchical wireless sensor network (WSN). Based on the theory of robust statistics (RS), a novel anti-outlier (extended) Kalman filter (KF) is presented for local state estimation in a clustered WSN with correlated measuring noises. In the fusion center (FC), a cross-covarianceindependent track fusion approach –- internal ellipsoidal approximation fusion (IEAF) is developed to fuse the local estimates, among which the correlations are usually unknown or incomplete. Simulation results illustrate the significance of the proposed approaches: the presented anti-outlier KF deteriorates in performances much less than the traditional KF (28.6% VS. 428.6%) in the presence of outlier; the proposed IEAF has higher fusion accuracy than the fusion estimator of covariance intersection (CI), and doesn’t need any prior knowledge.