An introduction to data fusion
2000
Abstract
Abstract In general, a fusion system is composed of sources of data, of means of acquisition of this data, of communications for the exchange of data, of intelligence to process data, update a dynamic model of the world and make decisions about further actions. In this paper, a definition of data fusion and sensor data fusion is worked out based on former publications.
References (33)
- M. Bares, D. Canamero, J. F. Delannoy, and Y. Kodrato . XPlans: Case-based Reasoning for Plan Recognition. Applied Arti cial Intelligence, 8(4):617{643, Oct.-Dec. 1994.
- T. Bass. Multisensor Data Fusion for Next Generation Distributed Intru- sion Detection Systems. Invited Paper, IRIS National Symposium on Sensor and Data Fusion, The Johns Hopkins University Applied Physics Laboratory, pages 24{27, May 1999.
- T. Bass. Intrusion Detection Systems and Multisensor Data Fusion: Creating Cyberspace Situational Awareness. Communications of the ACM, 43(4):99{ 105, May 2 0 0 0 .
- P. Bryanston-Cross, M. Burnett, D. D. Udrea, R. Marsh, B. H. Timmerman, A. Starr, and J. Estabon. INTErSECT: The Application of Data Fusion to a Multi Sensored Intelligent Engine. IEE Colloquium on Intelligent and Self-Validating Sensors, pages 6/1{6/11, June 1999.
- M. Bedworth and J. O'Brien. The Omnibus Model: a new model of data fusion? IEEE Aerospace and Electronics Systems Magazine, 15(4):30{36, April 2000.
- S. S. Blackman and T. J. Broida. Multiple Sensor Data Association and Fusion in Aerospace Applications. Journal of Robotic Systems, 7(3):445{485, June 1990.
- E. Bosse, J. Roy, and D. Grenier. Data Fusion Concepts Applied to a Suite of Dissimilar Sensors. Canadian Conference o n E l e ctrical and Computer En- gineering, 1996, 2:692{695, May 1 9 9 6 .
- J. R. Boyd. A Discourse on Winning and Losing. Unpublished set of brie ng slides available at Air University Library, Maxwell AFB, Alabama, M a y 1 9 8 7 .
- H. G. Bredemeyer and K. Bullock. Orthoptik. W alter de Gruyter, Berlin/New York, 1977.
- F. Butini, V. Cappellini, and S. Fini. Remote Sensing Data Fusion on Intelli- gent T erminals. European Transactions on Telecommunications and Related Technologies, 3(6):555{563, Nov.-Dec. 1992.
- S. P. Chaudhuri and R. D. Ferrante. Object Identi cation in a Simulated Battle eld Using Arti cial Intelligence Techniques. Proceedings of the 1983 IEEE Military Communications Conference, 3:673{677, 1983.
- Das97
- B. V. Dasarathy. Sensor Fusion Potential Exploitation-Innovative A r c hitec- tures and Illustrative Applications. Proceedings of the IEEE, 85:24{38, Jan. 1997.
- Tony Dodd. An Introduction to Multi-Sensor Data Fusion. ISIS research group, Department of Electronics & Computer Science, University of South- hampton, http://www.isis.ecs.soton.ac.uk/research/projects/khepri/tjd96r/, March 1998.
- I. Edwards, X. E. Gross, D. W. Lowden, and P. Strachan. Fusion of NDT Data. The British Journal of Non Destructive Testing, 35(12):710{713, Dec. 1993.
- U. Eysel. Klinische Neuroopthalmologie, chapter Zentrale Anteile der Se- hbahn, pages 10{21. Georg Thieme Verlag, Stuttgart, New York, 1998.
- K. E. Foote and D. J. Huebner. Error, Accuracy, and Precision. Technical re- port, The Geographer's Craft Project, Department of Geography, U n i v ersity of Texas at Austin, 1995.
- J. H. Graham. An Evidential Approach to Robot Sensory Fusion. Pro- ceedings of the 1986 IEEE International Conference on Systems, Man, and Cybernetics, 1:492{497, 1986.
- P. Grossmann. Multisensor Data Fusion. The GEC journal of Technology, 15:27{37, 1998.
- A. Huber. Klinische Neuroopthalmologie, chapter Periphere Anteile der Se- hbahn, pages 2{9. Georg Thieme Verlag, Stuttgart, New York, 1998.
- R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Transaction of the ASME, Series D, Journal of Basic Engineering, 82:35{45, March 1960.
- H. Kopetz, H. Kantz, G. Gr unsteidl, P. Puschner, and J. Reisinger. Tolerating Transient F aults in MARS. 20th. Symposium on Fault Tolerant Computing, Newcastle upon Tyne, UK, June 1990.
- Wald Lucien. Some Terms of Reference in Data Fusion. IEEE Transactions on Geoscience a n d R emote Sensing, 37(3), May 1 9 9 9 .
- R. C. Luo and K. L. Su. A Review of High-level Multisensor Fusion: Ap- proaches and Applications. Proceedings of the 1999 IEEE International Con- ference on Multisensor Fusion and Integration for Intelligent Systems, pages 25{31, 1999.
- G. T. McKee. What can be fused? Multisensor Fusion for Computer Vision, Nato Advanced Studies Institute Series F, 99:71{84, 1993.
- R. R. Murphy. Biological and Cognitive Foundations of Intelligent Sensor Fusion. IEEE Transactions on Systems, Man and Cybernetics, 26(1):42{51, Jan. 1996.
- P. J. Nahin and J. L. Pokoski. NCTR Plus Sensor Fusion Equals IFFN or Can Two Plus Two Equal Five? IEEE-Transactions-on-Aerospace-and- Electronic-Systems, AES-16(3):320{337, May 1 9 8 0 .
- B. Parhami. Multi-Sensor Data Fusion and Reliable Multi-Channel Compu- tation: Unifying Concepts and Techniques. Conference R ecord of the Twenty- Ninth Asilomar Conference on Signals, Systems and Computers, 1:745{749, Act.-Nov. 1995.
- N. S. V. Rao. A Fusion Method That Performs Better Than Best Sensor. Proceedings of the First International Conference on Multisource-Multisensor Information Fusion, pages 19{26, July 1998.
- A. N. Shulsky. Silent Warfare: Understanding the World of Intelligence. Brassey's, New York, 1991.
- C. R. Smith and G. J. Erickson. Multisensor Data Fusion: Concepts and Principles. IEEE Paci c Rim Conference on Communications, Computers and Signal Processing, 1:235{237, May 1 9 9 1 .
- J. von Neumann. Probabilistic Logics and the Syntheseis of Reliable Or- ganisms from Unreliable Components. In C. E. Shannon and J. McCarthy, Editors, Automata Studies, pages 43{98. Princeton University Press, 1956.
- E. Waltz and J. Llinas. Multisensor Data Fusion. Artech House, Norwood, Massachusetts, 1990.