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Notion and Structure of Sensor Data Fusion

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Tracking and Sensor Data Fusion

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Abstract

Sensor data fusion is an omnipresent phenomenon that existed prior to its technological realization or the scientific reflection on it. In fact, all living creatures, including human beings, by nature or intuitively perform sensor data fusion. Each in their own way, they combine or “fuse” sensations provided by different and mutually complementary sense organs with knowledge learned from previous experiences and communications from other creatures. As a result, they produce a “mental picture” of their individual environment, the basis of behaving appropriately in their struggle to avoid harm or successfully reach a particular goal in a given situation.

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References

  1. M.E. Liggins, D.L. Hall, J. Llinas (eds.), Handbook of Multisensor Data Fusion—Theory and Practice, 2nd edn. (CRC Press, Boca Raton, 2008)

    Google Scholar 

  2. E. Bosse, J. Roy, S. Wark, Concepts, Models, and Tool for Information Fusion (Artech House, Norwood, 2007)

    Google Scholar 

  3. B.V. Dasarathy (ed.), A special Issue on information fusion in computer security, in Information Fusion, vol 10, No. 4 (Elsevier, New York, 2009)

    Google Scholar 

  4. M. Wunder, J. Grosche (eds.), Verteilte Führungsinformationssysteme (Springer, New York, 2009)

    Google Scholar 

  5. L. Schmidt, C.M. Schlick, J. Grosche (eds.), Ergonomie und Mensch-Maschine-Systeme (Springer, Berlin, 2008)

    Google Scholar 

  6. D.S. Alberts, R.E. Hayes, Code of Best Practice for Experimentation (CCRP Publication series, Washington, DC, 2002)

    Google Scholar 

  7. H.L.V. Trees, K.L. Bell (eds.), Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking (Wiley-IEEE Press, New York, 2007)

    Google Scholar 

  8. H.W. Sorenson, Least-squares estimation: from Gauss to Kalman. IEEE Spectr. 7, 63–68 (1970)

    Google Scholar 

  9. K.-R. Biermann (ed.), Carl Friedrich Gauß in Briefen und Gesprächen (C. H. Beck, München, 1990)

    Google Scholar 

  10. E.T. Jaynes, Probability Theory: The Logic of Science (Cambridge University Press, Cambridge, 2003)

    Google Scholar 

  11. T. Bayes, An essay towards solving a problem in the doctrine of chances. Philos. Trans. Roy. Soc. London 53, 370418 (1763) (Reprinted in Biometrika, 45, 1958. http://www.stat.ucla.edu/history/essay.pdf)

    Google Scholar 

  12. N.L. Rabinovitch, Probability and Statistical Inference in Ancient and Medieval Jewish Literature (University of Toronto Press, Toronto, 1973)

    Google Scholar 

  13. S.M. Stigler, The History of Statistics (Harvard University press, Cambridge, 1986)

    Google Scholar 

  14. J. Wolfowitz, Abraham Wald, 1902–1950. Ann. Math. Stat. 23(1), 1–13 (1952)

    Google Scholar 

  15. A. Wald, Statistical Decision Functions (Wiley, New York, 1950)

    Google Scholar 

  16. S.L. Lauritzen, Thiele—Pioneer in Statistics (Oxford University Press, Oxford, 2002)

    Google Scholar 

  17. E. Brookner, Tracking and Kalman Filtering Made Easy (Wiley, Sudbury, 1998)

    Google Scholar 

  18. A. Gelb (ed.), Applied Optimal Estimation (M.I.T Press, Cambridge, 1974)

    Google Scholar 

  19. G. van Keuk, Description of a tracking strategy for phased array radar, in Proceedings of Advanced Radar Systems, NATO AGARD Advisory Group for Aerospace Research and Development Conference, No. 66, p. 36-1–36-10, Istanbul, Turkey, May 1970

    Google Scholar 

  20. R.A. Singer, Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Trans. Aerosp. Electron. Syst. TAES 5, 473–483 (1970)

    Article  Google Scholar 

  21. R.W. Sittler, An optimal data association problem in surveillance theory. IEEE Trans. Mil. Electron. MIL–8, 2 (1964)

    Google Scholar 

  22. D.B. Reid, An algorithm for tracking multiple targets. IEEE Trans. Autom. Control AC–24, 6 (1979)

    Google Scholar 

  23. G. van Keuk, PDAB—Ein Verfahren zur Einzelzielvervolgung in stark gestörter Umgebung. FFF Research Report 363, Wachtberg (1987)

    Google Scholar 

  24. S.S. Blackman, Multiple hypothesis methods. IEEE AES Mag. 19(1), 41–52 (2004)

    Article  Google Scholar 

  25. Y. Bar-Shalom, P.K. Willett, X. Tian, Tracking and Data Fusion. A Handbook of Algorithms (YBS Publishing, Storrs, 2011)

    Google Scholar 

  26. S.S. Blackman, R. Popoli, Design and Analysis of Modern Tracking Systems (Artech House, Norwood, 1999)

    MATH  Google Scholar 

  27. A. Farina, & F.A. Studer, Radar Data Processing, Vol. 1 Introduction and Tracking (Wiley, New York, 1985) (Vol. 2 Advanced Topics and Applications, 1986)

    Google Scholar 

  28. H.A.P. Blom, J. Jygeros (eds.), Stochastic Hybrid Systems: Theory and Safety Critical Applications (Springer, Berlin, 2006)

    Google Scholar 

  29. O.E. Drummond, Multiple-Object Tracking, PhD Thesis, University of California (1975)

    Google Scholar 

  30. L.D. Stone, T.L. Corwin, C.A. Barlow, Bayesian Multiple Target Tracking (Artech House, Norwood, 1999)

    MATH  Google Scholar 

  31. B. Ristic, S. Arulampalam, N. Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Radar Library, Norwood, 2004)

    Google Scholar 

  32. R.P.S. Mahler, Statistical Multisource-Multitarget Information Fusion (Artech House, Norwood, 2007)

    Google Scholar 

  33. R.L. Streit, Poisson Point Processes. Imaging, Tracking, and Sensing (Springer, New York, 2010)

    Google Scholar 

  34. H.B. Mitchell, Multi-Sensor Data Fusion (Springer, Berlin, 2007)

    Google Scholar 

  35. S. Stergiopoulos (ed.), Advanced Signal Processing: Theory and Implementation for Sonar, Radar, and Non-Invasive Medical Diagnostic Systems (CRC Press, Boca Raton, 2001)

    Google Scholar 

  36. F. Gustafsson, Statistical Sensor Fusion (Gazelle, Lancaster, 2010)

    Google Scholar 

  37. http://www.isif.org

  38. W.-D. Wirth, Radar Techniques Using Array Antennas (IEE Press, London, 2001)

    Book  Google Scholar 

  39. G. van Keuk, S. Blackman, On phased-array tracking and parameter control. IEEE Trans. Aerosp. Electron. Syst. AES–29(1), 186 (1993)

    Article  Google Scholar 

  40. R. Baltes, G. van Keuk, Tracking multiple maneuvering targets in a network of passive radars, in Proceedings of the IEEE International Radar Conference, RADAR’95, Washington DC (1995)

    Google Scholar 

  41. W. Koch (ed.), IEEE ISIF Sensor Data Fusion: Trends, Solutions, Applications. Annual Workshop published in IEEE Xplore. 2005: Bonn, 2006: Dresden, 2007: Bremen, 2009: Lübeck, 2010: Leipzig, 2011: Leipzig, since 2012: Bonn

    Google Scholar 

  42. W. Koch, Target tracking, Chap. 8, in Advanced Signal Processing: Theory and Implementation for Sonar, Radar, and Non-Invasive Medical Diagnostic Systems, ed. by S. Stergiopoulos (CRC Press, Boca Raton, 2001)

    Google Scholar 

  43. O.E. Drummond, Target tracking with retrodicted discrete probabilities. Proc. of SPIE Sig. Data Process Small Targets 3163, 249 (1997)

    Google Scholar 

  44. W. Koch, Fixed-interval retrodiction approach to Bayesian IMM-MHT for maneuvering multiple targets. IEEE Trans. Aerosp. Electron. Syst. AES–36(1), 2–14 (2000)

    Article  Google Scholar 

  45. W. Koch, J. Biermann, Anomaly detection from tracking-derived situation elements, in Processdings of the NATO Workshop on Data Fusion and Anomaly Detection for Maritime Situational Awareness—MSA 2009, La Spezia, Italy, September 2009

    Google Scholar 

  46. B. Ristic, B. La Scala, M. Morelande, & N. Gordon, Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction, in Proceedings of the 11th International Conference on Information Fusion, FUSION 2008, Cologne, July 2008

    Google Scholar 

  47. D. Fox, Activity recognition from wearable sensors. Keynote Lecture at 11th International Conference on Information Fusion, Cologne (2008)

    Google Scholar 

  48. S.C. Mukhopadhyay, H. Leung, P.K. Varshney, & R. Schober (eds.), Guest editorial special issue. IEEE Sens. J. Spec. Issue Cogn. Sens. Netw. 11(3), 519–521 (2010)

    Google Scholar 

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Authors and Affiliations

  1. SDF, Fraunhofer FKIE, Neuenahrer Straße 20, 53343, Wachtberg, Germany

    Wolfgang Koch

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  1. Wolfgang Koch
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Correspondence to Wolfgang Koch .

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Koch, W. (2014). Notion and Structure of Sensor Data Fusion. In: Tracking and Sensor Data Fusion. Mathematical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39271-9_1

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  • DOI: https://doi.org/10.1007/978-3-642-39271-9_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39270-2

  • Online ISBN: 978-3-642-39271-9

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