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Original scientific paper

Target tracking based on a multi-sensor covariance intersection fusion Kalman filter

Yulian Jiang
Jian Xiao

Full text: english PDF 445 Kb

page 47-54

downloads: 1.603



In a multi-sensor target tracking system, the
correlation of the sensors is unknown, and the
cross-covariance between the local sensors can not
be calculated. To solve the problem, the multisensor
covariance intersection fusion steady-state
Kalman filter is proposed. The advantage of the
proposed method is that the identification and
computation of cross-covariance is avoided, thus
the computational burden is significantly reduced.
The new algorithm gives an upper bound of the
covariance intersection fused variance matrix
based on the convex combination of local
estimations, therefore, ensures the convergence of
the fusion filter. The accuracy of the covariance
intersection (CI) fusion filter is lower than and
close to that of the optimal distributed fusion
steady-state Kalman filter, and is far higher than
that of each local estimator. A numerical example
shows that the covariance intersection fusion
Kalman filter has enough fused accuracy without
computing the cross-covariance.


multi-sensor system, covariance intersection fusion, distributed fusion, kalman filter

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