A robust estimation fusion with unknown cross-covariance in distributed systems

2009 ◽  
Author(s):  
Duzhi Wu ◽  
Jie Zhou ◽  
Xiaomei Qu
Keyword(s):  
Distributed Systems ◽  
Robust Estimation ◽  
Cross Covariance ◽  
Estimation Fusion

scholarly journals Minimax Robust Optimal Estimation Fusion for Distributed Multisensor Systems with a Relative Entropy Uncertainty

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Hua Li ◽  
Jie Zhou
Keyword(s):  
Robust Estimation ◽  
Relative Entropy ◽  
Estimation Error ◽  
Optimal Estimation ◽  
Minimum Variance ◽  
Estimation Errors ◽  
Multisensor Systems ◽  
Error Covariance ◽  
Local Estimation ◽  
Estimation Fusion

This paper considers the robust estimation fusion problem for distributed multisensor systems with uncertain correlations of local estimation errors. For an uncertain class characterized by the Kullback-Leibler (KL) divergence from the actual model to nominal model of local estimation error covariance, the robust estimation fusion problem is formulated to find a linear minimum variance unbiased estimator for the least favorable model. It is proved that the optimal fuser under nominal correlation model is robust while the estimation error has a relative entropy uncertainty.

scholarly journals Robust Estimation Fusion in Wireless Senor Networks with Outliers and Correlated Noises

2014 ◽  
Vol 10 (4) ◽  
pp. 393802 ◽  
Author(s):  
Yan Zhou ◽  
Dongli Wang ◽  
Tingrui Pei ◽  
Shujuan Tian
Keyword(s):  
Robust Estimation ◽  
Estimation Fusion ◽  
Correlated Noises

Robust Estimation Fusion

2012 ◽  
pp. 323-394
Author(s):  
Yunmin Zhu ◽  
Jie Zhou ◽  
Xiaojing Shen ◽  
Enbin Song ◽  
Yingting Luo
Keyword(s):  
Robust Estimation ◽  
Estimation Fusion

scholarly journals A robust fusion estimation with unknown cross-covariance in distributed systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Duzhi Wu ◽  
Aiping Hu
Keyword(s):  
Distributed Systems ◽  
Numerical Simulations ◽  
Minimax Problem ◽  
Performance Degradation ◽  
Tracking Performance ◽  
Fusion System ◽  
Estimation Errors ◽  
Numerical Examples ◽  
Cross Covariance ◽  
Better Than

Abstract An efficient robust fusion estimation (RFE) for distributed fusion system without knowledge of the cross-covariances of sensor estimation errors is suggested. With the hypothesis that the object lying in the intersection of some ellipsoids related to sensor estimations, the robust fusion estimation is designed to be a minimax problem, which is solved by proposing a novel relaxation strategy. Some properties of the RFE are discussed, and numerical simulations are also present to compare the tracking performance of RFE with that of the centralized fusion and CI method. The numerical examples show that the average tracking performance of RFE is slightly better than that of the CI method, and the performance degradation of RFE is acceptable compared with the centralized fusion.

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