JGIS  Vol.7 No.4 , August 2015
The Combination Operator of Information Sources by a New Expressive Matrix
ABSTRACT
The multi-sensors fusion refers to the synergistic combination of sensory data from multiple sensors to provide more accurate and reliable information. The potential benefits of the Fusion are multi-sensors’ redundancy and extra information acquired. The fusion of redundant information can reduce the overall uncertainty and thus helps to provide information specified more precisely. Several sensors providing redundant information can also be used to increase reliability in the case of error, omission or failure of sensors. The combination operators are exponential and are more complex in terms of calculation; the Dempster-Shafer operator is exponential for more than three (3) information sources [1] [2]. Our work focuses on the definition of another formulation of this operation, and puts it in a matrix form to illuminate the computational complexity, more precision guaranty and a minimal execution time. We propose to use each information source in a form of a matrix, which contains 0 value in lines that do not contain the masses (m(Ai) = 0) or once m(Ai) is not null (m(Ai) ≠ 0). The use of this expressed matrix attempts to ameliorate Dempster-Shafer operator via initialing either a criterion or criteria sources’ solution, increasing the efficiency of the Dempster-Shafer operator and facilitates the combination among the sources. We evaluate our approach by conducting a case study for showing the effectiveness of this matrix.

Cite this paper
Boualem, A. , Dahmani, Y. and Maatoug, A. (2015) The Combination Operator of Information Sources by a New Expressive Matrix. Journal of Geographic Information System, 7, 430-437. doi: 10.4236/jgis.2015.74034.
References
[1]   Martin, A. (2005) La fusion d’informations. Polycopié de cours ENSIETA, 1484.

[2]   Martin, A., Sévellec, G. and Leblond, I. (2004) Characteristics vs. Decision Fusion for Sea-Bottom Characterization. Journées d’Acoustique Sous-Marine, Brest, 19-20 October 2004.

[3]   Dempster, A.P. (1967) Upper and Lower Probabilities Induced by a Multi-Valued Mapping. Annals of Mathematical Statistics, 38, 325-339. http://dx.doi.org/10.1214/aoms/1177698950

[4]   Smets, P. (1990) The Combination of Evidence in the Transferable Belief Model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 447-458. http://dx.doi.org/10.1109/34.55104

[5]   Yager, R. (1987) On the Dempster-Shafer Framework and New Combination Rules. Information Sciences, 41, 93-138. http://dx.doi.org/10.1016/0020-0255(87)90007-7

[6]   Dubois, D. and Prade, H. (1986) On the Unicity of Dempster Rule of Combination. International Journal of Intelligent System, 1, 133-142. http://dx.doi.org/10.1002/int.4550010204

[7]   Dempster, A.P. (2008) The Dempster Shafer Calculus for Statisticians. International Journal of Approximate Reasoning, 48, 365-377. http://dx.doi.org/10.1016/j.ijar.2007.03.004

[8]   Dubois, D. and Prade, H. (1988) Representation and Combination of Uncertainty with Belief Functions and Possibility Measures. Computational Intelligence, 4, 244-264.
http://dx.doi.org/10.1111/j.1467-8640.1988.tb00279.x

[9]   Shafer, G. (1976) A Mathematical Theory of Evidence. Princeton University Press, Princeton.

 
 
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