Multi-Sensor Target Recognition Using VIKOR Combined with G1 Method

Article Preview

Abstract:

The aim of this paper is to propose a new multi-sensor target recognition method based on VIKOR method. Multi-sensor target recognition is an important part of information fusion. The multi-sensor target recognition problem contains many factors, and thus it is actually a multi-attribute decision problem. The new multi-sensor target recognition method combines the VIKOR method with G1 method. G1 method is an objectively determining weights method, and it does not need to test the consistency of judgment matrix, thus it is better than AHP. A practical example is studied to demonstrate the effectiveness and feasibility of the proposed method.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

321-324

Citation:

Online since:

December 2014

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] G. Girija, J. R. Raol, R, Appavraj and S. Kashyap. Tracking filter and multi-sensor data fusion. Sādhanā, Vol. 25 (2000), pp.159-167.

DOI: 10.1007/bf02703756

Google Scholar

[2] T. L. Chen and P. W. Que. Target recognition based on modified combination rule. Journal of Systems Engineering and Electronics, Vol. 17 (2006), pp.279-283.

Google Scholar

[3] O. Basir and X. H. Yuan. Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Information Fusion, Vol. 8 (2007), pp.379-386.

DOI: 10.1016/j.inffus.2005.07.003

Google Scholar

[4] S. Y. Chen and J. M. Hu. Variable fuzzy method and its application in parts recognition. Systems Engineering and Electronics, Vol. 28 (2006), pp.1325-1328.

Google Scholar

[5] G. P. Tu. Probability fusion method for the data from different sources. Journal of Transducer Technology, Vol. 21 (2000), pp.42-44.

Google Scholar

[6] Y. Liu, X. G. Gao and G. S. Lu. Multisensor target recognition based on the OWA aggregation operator. Journal of Transducer Technology, Vol. 19 (2006), pp.530-533.

Google Scholar

[7] L. C. Che, X. J. Zhou and Z. N. Xu. Application of extension method in multisensory data fusion for parts recognition. System Engineering Theory and Practics, Vol. 20 (2000), pp.91-94.

Google Scholar

[8] S. P. Wan. Method of interval deviation degree for uncertain multi-sensor target recognition. Control and Decision, Vol. 24 (2009), pp.1306-1309.

Google Scholar

[9] Y. J. Guo. Comprehensive Evaluation Theory and Method. (Science Press, Beijing 2002).

Google Scholar

[10] Opricovic S, Tzeng G H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, Vol. 156 (2004), pp.445-455.

DOI: 10.1016/s0377-2217(03)00020-1

Google Scholar

[11] S. Opricovic and G. H. Tzeng. Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, Vol. 178 (2007), pp.514-529.

DOI: 10.1016/j.ejor.2006.01.020

Google Scholar

[12] Y. Shao, F. C. Shi and J. Peng. An approach of robot non-vision multi-sensor fusion. Acta Electronica Sinica, Vol. 24 (1996). pp.94-97.

Google Scholar