An Information Fusion Algorithm Based on Kalman Filtering

Abstract:

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

• Periodical: Applied Mechanics and Materials (Volumes 444-445)
• Pages: 1072-1076
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.444-445.1072
• Online since: October 2013
• Authors: Xiu Hu Tan
• Keywords: Information Fusion, Kalman Filtering, Mean Square Error

References

[1] Ozbek I Y, Hasegawa-Johnson M, Demirekler M: IEEE Trans, Audio, Speech, and Language Processing. Vol. 19(5) (2011), p.1180.

DOI: 10.1109/tasl.2010.2087751

[2] Zhi-Qiang Zhang, Lian-Ying Ji, Zhi-Pei Huang: IEEE Trans, Systems, Man and Cybernetics, Part A: Systems and Humans. Vol. 42(5) (2012), p.1100.

[3] Govaers F, Koch W: IEEE Trans, Aerospace and Electronic Systems. Vol. 48(3) (2012), p.2718.

[4] Yilin Mo, Garone E, Casavola A: IEEE Trans, Automatic Control. Vol. 56(10) (2011), p.2489.

[5] Hu Haidong, Huang Xianlin: IEEE Trans, Systems Engineering and Electronics. Vol. 21(1) (2010), p.102.