Multi-Channel ARMA Signal Covariance Intersection Fusion Kalman Smoother

Abstract:

For the multi-channel ARMA signal with two sensors, by the classical Kalman filtering method and the covariance intersection (CI) fusion method, a covariance intersection fusion steady-state Kalman signal smoother is presented, which is independent of the unknown cross-covariance. It is proved that its accuracy is higher than that of each local Kalman signal smoother, and is lower than that of the optimal signal fuser weighted by matrices. The geometric interpretation of the above accuracy relations are presented based on the covariance ellipses. A simulation example result shows its effectiveness and correctness.

• Periodical: Advanced Materials Research (Volumes 655-657)
• Pages: 701-704
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.655-657.701
• Online since: January 2013
• Authors: Peng Zhang
• Keywords: Accuracy Relation, Covariance Ellipse, Covariance Intersection Fusion, Cross-Covariance, Information Fusion Kalman Smoother

References

[1] Y.B. Shalom, X.R. Li, Estimation with applications to tracking and navigation, Thiagalingam Kirubarajan, John Wiley & Sons. Inc, (2001).

[2] X.R. Li, Y.M. Zhu, J. Wang, C.J. Han, Unified optimal linear estimation fusion, Part I: Unified fusion rules, IEEE Trans. Information Theory. 49 (2003) 2192-2208.

DOI: 10.1109/tit.2003.815774

[3] S.L. Sun, Z.L. Deng, Multi-sensor optimal information Kalman filter, Automatica. 40 (2004) 1017-1023.

DOI: 10.1016/j.automatica.2004.01.014

[4] Z.L. Deng, Y. Gao, L. Mao, G. Hao, New approach to information fusion steady-state Kalman filtering, Automatica. 41 (2005) 1695-1707.

DOI: 10.1016/j.automatica.2005.04.020

[5] S.L. Sun, Multi-sensor optimal information fusion Kalman filters with applications, Aerospace Science and Technology. 8 (2004) 57-62.

DOI: 10.1016/j.ast.2003.08.003

[6] S.J. Julier , J.K. Uhlman, Non-divergent estimation algorithm in the presence of unknown correlations, Proceedings of the IEEE American Control Conference, Albuquerque, NM, USA (1997) 2369-2373.

DOI: 10.1109/acc.1997.609105

[7] X.J. Sun, Y. Gao, Z.L. Deng, C. Li, J.W. Wang, Multi-model information fusion Kalman filtering and white noise deconvolution, Information Fusion. 11 (2009) 163-173.

DOI: 10.1016/j.inffus.2009.06.004

[8] M.E. Liggins, J. Linas, Handbook of Multisensor Data Fusion Theory and Practice, Second Edition. CRC Press, (2009).

[9] L.J. Chen, P.O. Arambel, C. Rago, R.K. Mehra, Estimation under unknown correlation: Covariance intersection revisited, IEEE Trans. Automatic Control. 47 (2002) 1879-1882.

DOI: 10.1109/tac.2002.804475

[10] Z.L. Deng, Multisensor information fusion filtering theory with applications, Harbin Institute of Technology Press, (2007).

[11] Y.X. Yuan, W.Y. Sun, Optimization Theory and Method, Science Press, Beijing, (2003).