DocumentCode
2775306
Title
Information fusion estimation of noise statistics for multisensor systems
Author
Gao, Yuan ; Wang, Weiling ; Deng, Zili
Author_Institution
Dept. of Autom., Heilongjiang Univ., Harbin, China
fYear
2009
fDate
17-19 June 2009
Firstpage
1116
Lastpage
1120
Abstract
For multisensor linear discrete time invariant system with unknown noise statistics and correlated noises, by the correlation method, the online local estimators of noise variances, correlated matrices and cross covariances can be obtained by solving the different partial correlated function matrix equations. The information fusion noise statistics estimators are presented by averaging the local estimators of noise statistics. Based on the ergodicity of the sample correlated function, it is proved the local and fused estimators of noise statistics are strong consistent, i.e. they converge to corresponding true values with probability one. They can be applied to design the self tuning information fusion filters. A simulation example of three-sensor system with correlated noises shows the effectiveness of the fused estimation.
Keywords
correlation methods; discrete time systems; estimation theory; linear systems; matrix algebra; sensor fusion; signal denoising; statistics; correlated function, ergodicity; correlated noises; cross-covariances; information fusion estimation; linear discrete time invariant system; multisensor systems; noise statistics; partial correlated function matrix equation; self tuning information fusion; Automation; Autoregressive processes; Information filtering; Kalman filters; Multisensor systems; Parameter estimation; Probability; State estimation; Statistics; Technological innovation; Correlated Method; Estimators of Noise Statistics; Information Fusion Estimation; Strong Consistence;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location
Guilin
Print_ISBN
978-1-4244-2722-2
Electronic_ISBN
978-1-4244-2723-9
Type
conf
DOI
10.1109/CCDC.2009.5191542
Filename
5191542
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