Analysis and Modeling of Uncertain System in Complex Environment

Article Preview

Abstract:

In complex battlefield environment all kinds of the uncertainties for target tracking make the analysis and modeling of tracking system irresolvable. Aiming at different levels of system uncertainty, the correspondent modeling methods are introduced. First, the stochastic process model is investigated which includes target process equation and sensor measurement equation. Second, joint state-class probabilistic density is given for realizing target tracking and classification simultaneously. Finally, the joint target detection, tracking and identification based on finite-set statistics theory is represented for multi-sensor multi-target tracking.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

973-976

Citation:

Online since:

October 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] R. Mahler. An introduction to multisource-multitarget statistics and its applications. Lockheed Martin, Eagan, MN, Technical Monograph, (2000).

Google Scholar

[2] R. Mahler. Statistical multisource multitarget information fusion. Norwood MA: Artech House, (2007).

Google Scholar

[3] S. Blackman, R. Popoli. Design and analysis of modern tracking systems. Artech House, Norwood, (1999).

Google Scholar

[4] Y. Bar-Shalom, X. R. Li. Multitarget-multisensor tracking: principles and techniques. YBS Publishing, Storrs, (1995).

Google Scholar

[5] W. Mei, G. L. Shan, and X. -R. Li. Simultaneous tracking and classification: a modularized scheme. IEEE Trans. on Aerospace and Electronic Systems. 2007, 43(2): 487-506.

DOI: 10.1109/taes.2007.4285355

Google Scholar

[6] Y. Bar-Shalom, X. R. Li, and T. Kirubarajan. Estimation with applications to tracking and navigation. John Wiley & Sons, New York, (2001).

Google Scholar

[7] D. H. Nguyen, J. H. Kay, and B. J. Orchard. Classification and tracking of moving ground vehicles. Lincoln Laboratory Journal. 2002, 13(2): 275-308.

Google Scholar

[8] J. Goutsias, R. Mahler, and H. Nguyen. Random sets theory and applications. New York: Wiley, (2003).

Google Scholar