Design of information fusion filter for a class of multi-sensor asynchronous sampling systems

For the discrete-time linear stochastic systems with multiple sensors of different sampling rates, a distributed information fusion filter weighted by matrices is given in the linear minimum variance sense. The correlation of estimation errors between any two local filters is considered. Compared with the fusion filter where the correlation of local estimation errors isn't taken into account, the proposed filter improves the accuracy. An example shows the feasibility and effectiveness of the proposed algorithm.

[1]  Shu-li Sun,et al.  Multi-sensor optimal information fusion Kalman filters with applications , 2004 .

[2]  Shu-Li Sun,et al.  Distributed optimal component fusion weighted by scalars for fixed-lag Kalman smoother , 2005, Autom..

[3]  Quan Pan,et al.  Multi-rate optimal state estimation , 2009, Int. J. Control.

[4]  Lang Hong.  Multiresolutional filtering using wavelet transform , 1993 .

[5]  L.P. Yan,et al.  Asynchronous multirate multisensor information fusion algorithm , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Yuanqing Xia,et al.  The modeling and estimation of asynchronous multirate multisensor dynamic systems , 2013, Proceedings of the 32nd Chinese Control Conference.

[7]  L. Hong.  Multiresolutional distributed filtering , 1994, IEEE Trans. Autom. Control..

[8]  N. A. Carlson.  Federated square root filter for decentralized parallel processors , 1990 .

[9]  K. H. Kim,et al.  Development of track to track fusion algorithms , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[10]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.