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Fusing sensor information using fuzzy measures

Published online by Cambridge University Press:  09 March 2009

Hans Odeberg
Hans Odeberg
Affiliation:
Department of Physics and Measurement Technology, Linkoping Institute of Technology, S–581 83 Linköping (Sweden)
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Summary

In a measurement system with intelligent, distributed sensor processes, complementary observations from different sensor need be combined with each other. This paper describes a method based on fuzzy measures, in which a global ‘fusion algorithm’ questions the sensors as to their support and opinion of a hypothesis. The sensor opinions are clustered into groups based on their support of each others' opinions, and fused using a new fuzzy operator.

Keywords

Sensor informationFuzzy measuresFusion algorithmComplex system

Information

Type
Article
Information
Robotica , Volume 12 , Issue 5 , September 1994 , pp. 465 - 472
Copyright
Copyright © Cambridge University Press 1994

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References

1.Krotkov, E. and Kories, R., ‘Adaptive Control of Cooperating Sensors: Focus and Stereo Ranging with an Agile Camera System’ Proceedings IEEE Conference on Robotics and Automation,Philadelphia(April, 1988) pp. 548–553.CrossRefGoogle Scholar
2.Durrant-Whyte, H.F., Integration, Coordination and Control of Multi-Sensor Robot Systems (Kluwer Academic Publishers, Boston, 1988).Google Scholar
3.Hackett, J. and Shah, M., ‘Multi-Sensor Fusion: A Perspective’ Proceedings IEEE Conference on Robotics and Automation,Philadelphia(April, 1988) pp. 1324–1330.Google Scholar
4.Luo, R., Lin, M. and Scherp, R., ‘Dynamic Multi-Sensor Data Fusion System for Intelligent Robots’ Proceedings IEEE Conference on Robotics and Automation,Philadelphia(April, 1988) pp. 386–396.CrossRefGoogle Scholar
5.Weerahandi, S. and Zideh, J.V., ‘Elements of Multi- Bayesian Decision Theory’ The Annals of Statistics 11, No. 4, 1032–1046 (1983).Google Scholar
6.Zadeh, L., “Fuzzy Logic” Computer 21, No. 4, 83–93 (1988).Google Scholar
7.Odeberg, H., ‘Distance Measurements for Fuzzy Sensor Opinions’ Measurement Science & Technology 4, No. 8, 808–815 (1993).Google Scholar
8.Duda, R. and Hart, A., Pattern Recognition and Scene Analysis (John Wiley, New York, 1973).Google Scholar
9.Bezdek, J., Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York, 1981).CrossRefGoogle Scholar
10.Schmucker, K., Sets, Fuzzy, Natural Language Computations, and Risk Analysis (Computer Science Press, Rockville, Md., 1984).Google Scholar
11.Dubois, D. and Prade, H., Possibility Theory-an Approach to Computerized Processing of Uncertainty (Plenum Press, New York, 1988).Google Scholar