Distributed Fusion Filtering for Multi-sensor Networked Uncertain Systems With Unknown Communication Disturbances and Compensations of Packet Dropouts
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摘要: 研究了带有未知通信干扰、观测丢失和乘性噪声不确定性的多传感器网络化系统的状态估计问题.通过白色乘性噪声描述系统状态和观测中的随机不确定性，采用一组服从Bernoulli分布的随机变量描述网络传输过程中存在的观测丢失现象，且数据传输中存在未知的网络通信干扰.当发生丢包时，以当前丢失观测的预报值进行补偿.对每个单传感器子系统，应用线性无偏最小方差估计准则设计了不依赖于未知通信干扰的最优线性滤波器.推导了任两个局部滤波误差之间的互协方差阵.进而，应用矩阵加权融合估计算法给出了分布式融合状态滤波器.仿真例子验证了算法的有效性.Abstract: This paper is concerned with the state estimation problem for multi-sensor networked systems with unknown communication disturbances, measurement losses and multiplicative noise uncertainties. The random uncertainties of the state and measurements of systems are described by white multiplicative noises. The phenomena of measurement losses during data transmissions through networks are described by a group of Bernoulli distributed random variables. Unknown communication disturbances exist in data transmissions. The predictors of the lost measurements are used as the compensation in the presence of packet losses. By applying the linear unbiased minimum variance estimation criterion, an optimal linear filter independent of unknown communication disturbances is designed for every single sensor subsystem. Filtering error cross-covariance matrices between any two local filters are derived. Further, a distributed fusion state filter is presented by using the matrix-weighted fusion estimation algorithm. A simulation example is given to verify the effectiveness of the proposed algorithms.1) 本文责任编委 高会军
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图 3 局部与分布式融合状态滤波器估计误差方差比较图
Fig. 3 Comparison of estimation error variances of local and distributed fusion state filters
图 4 带补偿与无补偿的第3传感器子系统滤波器的MSE比较
Fig. 4 MSE comparison of the 3rd sensor subsystem filters with compensation and no compensation
图 5 带补偿与无补偿的分布式融合滤波器的MSE比较
Fig. 5 MSE comparison of distributed fusion filters with compensation and no compensation
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