Abstract
To handle the estimation fusion problem between local estimation errors for the system with unknown cross-covariances and to avoid a large computed burden and computational complexity of cross-covariances, for a two-sensor linear discrete time-invariant stochastic system with time-delayed measurements, by the measurement transformation method, an equivalent system without measurement delays is obtained and then using the Covariance Intersection (CI) fusion method, the covariance intersection steady-state Kalman fuser is presented. It is proved that its accuracy is higher than that of each local estimator and is lower than that of optimal Kalman fuser weighted by matrices with known cross-covariances. A Monte-Carlo simulation example shows the above accuracy relation and indicates that its actual accuracy is close to that of the Kalman fuser weighted by matrices, hence it has good performances.
Highlights
Multisensor information has been applied to many fields, such as Guidance, defense, robotics, tracking, signal processing (Hall and Llinas, 1997; Li et al, 2003; Bar-Shalom and Li, 2001)
To compute the optimal distributed weighted fusion Kalman estimators (Sun and Deng, 2004; Bar-Shalom and Campo, 1986), it is required to compute the crosscovariances among local Kalman estimator errors, which needs a large computational burden, because these cross-covariances are generally unknown (Deng et al, 2008) or their computation is very complex (Sun and Deng, 2009; Sun et al, 2009; Liggins et al, 2009)
In order to overcome the difficulty and complexity of computing crosscovariances, by the Covariance Intersection (CI) method (Julier and Uhlmann, 1997; Chen et al, 2002; Julier and Uhlmann, 1996, 2009; Guo et al, 2010; Bolzani et al, 2007), a CI fusion steady-state Kalman fuser is presented, whose objective is to avoid to find the cross-covariances and reduce the computational burden. It can handle the fused estimation problem of the system with unknown cross-covariances and it has higher accuracy comparing with local estimators
Summary
Multisensor information has been applied to many fields, such as Guidance, defense, robotics, tracking, signal processing (Hall and Llinas, 1997; Li et al, 2003; Bar-Shalom and Li, 2001). In order to overcome the difficulty and complexity of computing crosscovariances, by the CI method (Julier and Uhlmann, 1997; Chen et al, 2002; Julier and Uhlmann, 1996, 2009; Guo et al, 2010; Bolzani et al, 2007), a CI fusion steady-state Kalman fuser is presented, whose objective is to avoid to find the cross-covariances and reduce the computational burden It can handle the fused estimation problem of the system with unknown cross-covariances and it has higher accuracy comparing with local estimators. The CI fuser is consistent, so it has the good performance
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