DocumentCode :
567493
Title :
Research on ellipsoidal intersection fusion method with unknown correlation
Author :
Wu, Tao-Tao ; An, Jin ; Ding, Chun-Shan ; Luo, Shuang-Xi
Author_Institution :
Jiang-Su Autom. Res. Inst., Lian-Yun-Gang, China
fYear :
2012
fDate :
9-12 July 2012
Firstpage :
558
Lastpage :
564
Abstract :
This paper reviews the advantages and shortages of the covariance intersection (CI) and ellipsoidal intersection (EI) methods for decentralized state fusion with unknown correlation, and makes some progress on both of them. New results are: a). For CI method, the convexity property is proved for the two classical cost functions (i.e., trace and natural logarithm of determinant of the fused covariance), and a simple form of the optimization conditions is derived for the latter one. Furthermore, a fast 2-sensor CI algorithm is proposed by expressing the cost function in scalar form. b). For the 2-sensor EI algorithm which minimized the natural logarithm of determinant of the mutual covariance, a new proof for its optimality is presented, which partly makes up the gap in [17]. Simulation results show the efficiency for both the 2-sensor CI and EI algorithms.
Keywords :
Kalman filters; 2-sensor CI algorithm; Kalman filter; convexity property; cost functions; covariance intersection; decentralized state fusion methods; ellipsoidal intersection fusion method; fused covariance; mutual covariance; natural logarithm; unknown correlation; Algorithm design and analysis; Approximation algorithms; Correlation; Cost function; Optimized production technology; Software algorithms; Covariance intersection; Newton iteration; algorithm; cost function; ellipsoidal intersection; restricted convex programming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2012 15th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4673-0417-7
Electronic_ISBN :
978-0-9824438-4-2
Type :
conf
Filename :
6289851
Link To Document :
بازگشت