A Novel Information Fusion Method Based on Preference Selection Index


Article Preview

The aim of this paper is to propose a new information fusion method for the problem of multi-sensor target recognition. Multi-sensor information fusion problem contains many characteristic indexes, and thus it can be regarded as a multi-attribute decision making problem. The new fusion method is put forward based on preference selection index method. The new information fusion method is not necessary to assign relative importance between attributes, but overall preference value of attributes are calculated using concept of statistics. Thus the new method can overcome the subjective randomness of subjectively weighting method. An applied example proves that the method is both effective and exercisable.



Edited by:

Helen Zhang, M. Han and X.J. Zhao




H. Zheng, "A Novel Information Fusion Method Based on Preference Selection Index", Advanced Materials Research, Vol. 1078, pp. 349-352, 2015

Online since:

December 2014





* - Corresponding Author

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

DOI: https://doi.org/10.1007/bf02703756

[2] P. L. Begler. Shafer-Dempster reasoning with application to multi-sensor target identification system. IEEE Trans on SMC, Vol. 17 (1997), pp.968-977.

[3] 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.

[4] 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: https://doi.org/10.1016/j.inffus.2005.07.003

[5] J. Chanussot, G. Mauris, P. Lambert. Fuzzy fusion techniques for linear features detection in multi-temporal SAR images. IEEE Trans. on Geosci. and Remote Sensing, Vol. 37 (1999), pp.1292-1305.

DOI: https://doi.org/10.1109/36.763290

[6] 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.

[7] S. P. Wan and G. P. Tu. Method of discriminatory analysis for multi-sensors data. Journal of Transducer Technology, Vol. 21 (2000), pp.27-29.

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

[9] 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.

[10] 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.

[11] K. Maniya and M. G. Bhatt. A selection of material using a novel type decision-making method: preference selection index method. Materials and Design, Vol. 31 (2010), pp.1785-1789.

DOI: https://doi.org/10.1016/j.matdes.2009.11.020

[12] K. Maniya and M.G. Bhatt. An alternative multiple attribute decision making methodology for solving optimal facility layout design selection problems. Comput. Ind. Eng., 61 (2011), pp.542-549.

DOI: https://doi.org/10.1016/j.cie.2011.04.009

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

[14] 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.

[15] H. P. Ren and L. W. Yang, Multi-sensor target recognition based on VIKOR. Sensors and Transducers, Vol. 156 (2013), pp.130-135.