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
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;
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
DOI :
10.1109/CCDC.2009.5191542