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Data Fusion and Standard Techniques

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Mathematics of Data Fusion

Part of the book series: Theory and Decision Library ((TDLB,volume 37))

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Abstract

Data fusion, or information fusion as it is also known, is the name which has been attached to a variety of interrelated problems arising primarily in military applications. Condensed into a single statement, data fusion might be defined thusly:

Locate and identify an unknown number of unknown objects of many different types on the basis of different kinds of evidence. This evidence is collected on an ongoing basis by many possibly re-allocatable sensors having varying capabilities. Analyze the results in such a way as to supply local and over-all assessments of the significance of a scenario and to determine proper responses based on those assessments.

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Bibliography

  1. Adams, E. W. (1966) Probability and the logic of conditionals, in Aspects of Inductive Logic, J. Hintikka and P. Suppes (eds.), North-Holland, Amsterdam, pp. 265–316.

    Google Scholar 

  2. Adams, E. W. (1975) The Logic of Conditionals, North Holland, Dordrecht.

    MATH  Google Scholar 

  3. Aigner, M. (1979) Combinatorial Theory, Springer-Verlag, New York.

    Book  MATH  Google Scholar 

  4. Alhakeem, S. and Varshney, P. K. (1995), A Unified approach to the design of decentralized detection systems, IEEE Transactions on Aerospace and Electronic Systems, 31, 9–20.

    Article  Google Scholar 

  5. Alspach, D. L. (1975) A Gaussian Sum Approach to the Multi-Target Identification-Tracking Problem, Automatica, 11, 285–296.

    Article  MATH  Google Scholar 

  6. Antony, R. T. (1995) Principles of data fusion automation, Artech House, Boston.

    Google Scholar 

  7. Barlow, C. A., Stone, L. D., and M.V. Finn (1996) Unified data fusion, Proceedings of the Ninth National Symposium on Sensor Fusion, I ( Unclassified), Naval Postgraduate School, Monterey CA. To appear.

    Google Scholar 

  8. Bar-Shalom, Y. ed. (1990) Multitarget-Multisensor Tracking: Advanced Applications, Artech House, Boston.

    Google Scholar 

  9. Bar-Shalom, Y. (1978) Tracking methods in a multitarget environment, IEEE Transactions on Automatic Control, AC-23, 618–626.

    Google Scholar 

  10. Bar-Shalom, Y. and Fortmann, T. E. (1988) Tracking and Data Association, Academic Press, New York.

    MATH  Google Scholar 

  11. Bar-Shalom, Y. and Li, X. R. (1993) Estimation and Tracking: Principles, Techniques, and Software, Artech House, Boston.

    MATH  Google Scholar 

  12. Bar-Shalom, Y., and Li, X.-R. (1995) Multitarget-Multisensor Tracking: Principles and Techniques, YBS (private publication)

    Google Scholar 

  13. Black, P. K. (1996) An Examination of Belief Functions and Other Monotone Capacities, Doctoral Dissertation, Carnegie-Mellon University

    Google Scholar 

  14. Blackman, S. S. (1988) Multiple-Target Tracking with Radar Applications, Artech House, Dedham MA.

    Google Scholar 

  15. Bonissone P. P. and Wood, N. C. (1989) T-norm based reasoning in situation assessment applications, in L.N. Kanal, T.S. Levitt, and J.F. Lemmer, eds., Uncertainty in Artificial Intelligence 3, Elsevier, pp. 241–256.

    Google Scholar 

  16. Boole, G. (1854) An Investigation of The Laws of Thought, Walton Maberly, London; reprinted 1951, Dover Publications, New York.

    Google Scholar 

  17. Chaffee, S., Poore, A. B., Rijavec, R., Gassner, R., and Vannicola, V. C. A centralized fusion multisensor/multitarget tracker based on multidimensional assignments for data association, SPIE Proceedings, 2484, 114–125.

    Google Scholar 

  18. Calabrese, P. G. (1987) An algebraic synthesis of the foundations of logic and probability, Information Sciences, 42, 187–237.

    Article  MathSciNet  MATH  Google Scholar 

  19. Chong, C. Y., Mori, S., and Chang, K. C. (1990) Distributed multitarget multisensor tracking, in Y. Bar-Shalom, ed., Multitarget-Multisensor Tracking: Advanced Applications, Artech House, Chapter 8.

    Google Scholar 

  20. Choquet, G. Theory of Capacities, Ann. Inst. Fourier,5 (1953/54), 131–295.

    Google Scholar 

  21. Copeland, A. H. (1950) Implicative boolean algebras, Mathematische Zeitschrift, 53 (3), 285–290.

    Article  MathSciNet  MATH  Google Scholar 

  22. Daley, D. J., and Vere-Jones, D. (1988) An Introduction to the Theory of Point Processes, Springer-Verlag.

    Google Scholar 

  23. Daum, F. (1992) A system approach to multiple target tracking, in Y. Bar-Shalom (ed.), Multitarget-Multisensor Tracking: Applications and Advances, Vol. II, Artech House, Dedham MA.

    Google Scholar 

  24. DeFinetti, B. (1936) La logique de la probabilite in Actes du Congres International de Philosophie Scientifique, Hermann et Cie (eds.), Paris, pp. IV1–IV9.

    Google Scholar 

  25. DeFinetti, B. (1974) Theory of Probability, volume I, II, J. Wiley, New York.

    Google Scholar 

  26. Dubois, D., and Prade, H. (1990) Consonant approximations of belief functions, International Journal of Approximate Reasoning, 4, 419–449.

    Article  MathSciNet  MATH  Google Scholar 

  27. Dubois, D., and Prade, H. (1994) Fuzzy sets—a convenient fiction for modeling vagueness and possibility, IEEE Transactions on Fuzzy Systems, 2 (1), 22–26.

    Article  Google Scholar 

  28. Dubois, D. and Prade, H. (1994) Conditional objects as nonmonotonic consequence relationships IEEE Transactions on Systems, Man and Cybernetics,24(12), 1724–1740.

    Google Scholar 

  29. Durrett, R. (1996) Probability: Theory and Examples, Second Edition, Duxbury Press, Belmont.

    Google Scholar 

  30. Eells, E. and Skyrms, B. (1994) Probability and Conditionals, Cambridge University Press, Cambridge.

    MATH  Google Scholar 

  31. Fagin, R. and Halpern, J. Y. (1991) A new approach to updating beliefs, in P.P. Bonissone, M. Henrion, and J.F. Lemmer eds., Uncertainty in Artificial Intelligence 6, pp. 347–374.

    Google Scholar 

  32. Fagin, R. and Halpern, J. Y. (1991) Uncertainty, belief, and probability, Computer Intelligence, 7, 160–173.

    Article  Google Scholar 

  33. Fishman, P. M. and Snyder, D. L. (1976) The statistical analysis of space-time point processes, IEEE Transactions on Information Theory, IT-22, 257–274.

    Google Scholar 

  34. Fixsen, D. and Mahler, R. (1997) The modified Dempster-Shafer approach to classification, IEEE Transactions on Systems, Man and Cybernetics, 27 (1), 96–104.

    Article  Google Scholar 

  35. Fortmann, T. E., Bar-Shalom, Y., and Scheffe, M. (1983) Sonar tracking of multiple targets using joint probabilistic data association, IEEE Journal of Oceanic Engineering, 0E-8, 173–183.

    Google Scholar 

  36. Goicoechea, A. (1988) Expert system models for inference with imperfect knowledge: a comparative study, Journal of Statistical Planning and Inference, 20, 245–277.

    Article  MATH  Google Scholar 

  37. Goodman, I. R. (1979) A general model for the multiple target correlation and tracking problem, Proceedings of the 18th IEEE Conference on Decision and Control, Fort Lauderdale FL, pp. 383–388.

    Google Scholar 

  38. Goodman, I. R. (1982) Fuzzy sets as equivalence classes of random sets, in R. Yager, ed., Fuzzy Sets and Possibility Theory, Permagon, pp. 327–343.

    Google Scholar 

  39. Goodman, I. R. (1987) A general theory for the fusion of data, Proceedings of the 1987 Joint Services Data Fusion Symposium,.

    Google Scholar 

  40. Goodman, I. R. (1994) A new characterization of fuzzy logic operators producing homomorphic-like relations with one-point coverages of random sets, in P.P. Wang, ed., Advances in Fuzzy Theory and Technology, Vol. 2, Duke University, Durham, pp. 133–159.

    Google Scholar 

  41. Goodman, I. R. (1986) Pact: an approach to combining linguistic-based and probabilistic information in correlation and tracking, Technical Document 878,March 1986,Naval Ocean Command and Control Ocean Systems Center, RDTE Division, San Diego; and “A Revised Approach to Combining Linguistic and Probabilistic Information in Correlation,” Technical Report 1386, July 1992,Naval Ocean Command and Control Ocean Systems Center, RDTE Division, San Diego.

    Google Scholar 

  42. Goodman, I. R. (1994) Toward a comprehensive theory of linguistic and probabilistic evidence: Two new approaches to conditional event algebra,“ IEEE Transactions on Systems, Man and Cybernetics, 24, 1685–1698.

    Article  Google Scholar 

  43. Goodman, I. R. (1983) A unified approach to modeling and combining of evidence through random set theory, Proceedings of the Sixth MIT/ONR Workshop on CS Systems, Boston, MA, pp. 42–47.

    Google Scholar 

  44. Goodman, I. R. (1987) A measure-free approach to conditioning, Proceedings Third AAAI Workshop on Uncertainty in Artificial Intelligence, University of Washington at Seattle.

    Google Scholar 

  45. Goodman, I. R. (1991) Evaluation of combinations of conditioned information: a history,Information Sciences, 57–58, 79–110.

    Google Scholar 

  46. Goodman, I. R. (1991) Similarity measures of events, relational events algebra and extensions to fuzzy logic, 1996 Biennial Conference of the North American Fuzzy Information Processing Society–NAFIPS, University of California at Berkeley, pp. 187–191.

    Google Scholar 

  47. Goodman, I. R. and Kramer, G. F. (1996) Extension of relational event algebra to a general decision making setting, Proceedings of the Conference on Intelligent Systems: A Semiotic Perspective, National Institute of Standards and Technology (NIST), Gaithersberg, MD, to appear.

    Google Scholar 

  48. Goodman, I. R. and Kramer, G. F. (1996) Applications of relational event algebra to the development of a decision aid in Command and Control, Proceedings of 1996 Command and Control Research and Technology Symposium, Naval Postgraduate School, Monterey, CA, pp. 415–435.

    Google Scholar 

  49. Goodman, I. R. and Kramer, G. F. (1996) Extension of relational and conditional event algebra to random sets with applications to data fusion, Proceedings of the Workshop on Applications and Theory of Random Sets,Institute for Mathematics and Its Applications (IMA), University of Minnesota at Minneapolis, to appear.

    Google Scholar 

  50. Goodman, I. R. and Nguyen, H. T. (1985) Uncertainty Models for Knowledge Based Systems,North-Holland, Amsterdam.

    Google Scholar 

  51. Goodman, I. R., Nguyen, H. T., and Walker, E. A. (1991) Conditional Inference and Logic for Intelligent Systems: A Theory of Measure-Free Conditioning, North-Holland.

    Google Scholar 

  52. Goodman, I. R. and Nguyen, H. T. (1993,1994) A theory of conditional information for probabilistic inference in intelligent systems: II product space approach; III mathematical appendix, Information Sciences, 76(1,2), 13–42; 75(3), 253–277.

    Google Scholar 

  53. Goodman, I. R. and Nguyen, H. T. (1995) Mathematical foundations of conditionals and their probabilistic assignments, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 3 (3), 247–339.

    Article  MathSciNet  MATH  Google Scholar 

  54. Grabisch, M., Nguyen, H. T., and Walker, E. A. (1995) Fundamentals of Uncertainty Calculi With Applications to Fuzzy Inference, Kluwer Academic Publishers, Dordrecht.

    Google Scholar 

  55. Gupta, M. M., Qi, J. (1991) Connectives (AND, OR, NOT) and T-operators in fuzzy reasoning, in I.R. Goodman, M.M. Gupta, H.T. Nguyen, and G.I. Rogers (eds.), Conditional Logic in Expert Systems, pp. 211–233.

    Google Scholar 

  56. Hailperin, T. (1986) Boole’s Logic and Probability,(Second Edition), North-Holland, Amsterdam.

    Google Scholar 

  57. Hailperin, T. (1996) Sentential Probability Logic,Lehigh University Press, Bethlehem.

    Google Scholar 

  58. Hall, D. L. (1992) Mathematical Techniques in Multisensor Data Fusion,Artech House, Dedham, MA.

    Google Scholar 

  59. Halpern, J. Y. and Fagin, R. (1992) Two views of belief: belief as generalized probability and belief as evidence, Artificial Intelligence, 54, 275–317.

    Article  MathSciNet  MATH  Google Scholar 

  60. Hestir, K., Nguyen, H. T., and Rogers, G. S. (1991) A random set formalism for evidential reasoning, in I.R. Goodman, M.M. Gupta, H.T. Nguyen and G.S. Rogers, eds., Conditional Logic in Expert Systems, North-Holland, Amsterdam, pp. 309–344.

    Google Scholar 

  61. Höhle, U. (1981) A mathematical theory of uncertainty: fuzzy experiments and their realizations, in R.R. Yager (ed.), Recent Developments in Fuzzy Set and Possibility Theory, Permagon Press, 344–355.

    Google Scholar 

  62. Höhle, U. (1981) Representation theorems for L-fuzzy quantities, Fuzzy Sets and Systems, 5, 83–107.

    Article  MathSciNet  MATH  Google Scholar 

  63. Johnson, R. W. (1986) Independence and Bayesian updating methods, in L.N. Kanal and J.F. Lemmer, eds., Uncertainty in Artificial Intelligence, Elsevier, pp. 197–201.

    Google Scholar 

  64. Kamen, E. W. (1992) Multiple target tracking based on symmetric measurement equations, IEEE Transactions on Automatic Control, 37, 371–374.

    Article  MathSciNet  Google Scholar 

  65. Kamen, E. W., Lee, Y. J., and Sastry, C. R. (1994) A parallel SME filter for tracking multiple targets in three dimensions, SPIE Proceedings, 2235, 417–428.

    Article  Google Scholar 

  66. Kamen, E. W. and Sastry, C. R. (1993) Multiple target tracking using products of position measurements, IEEE Transactions on Aerospace and Electronics Systems, 29, 476–493.

    Article  Google Scholar 

  67. Kamen, E. W., Sastry, C. R., and Sword, C. K. (1997) The SME filter approach to sensor fusion in multiple target tracking, Proceedings of the 1993 SPIE Conference on Signal and Data Processing of Small Targets, to appear.

    Google Scholar 

  68. Kantor, P. B. (1991) Orbit space and closely spaced targets, Proceedings of the SDI Panels on Tracking, No. 2.

    Google Scholar 

  69. Kappos, D. A. (1969) Probability Algebra and Stochastic Processes,Academic Press, New York. pp. 16–17 et passim.

    Google Scholar 

  70. Kastella, K. (1995) Event averaged maximum likelihood estimation and mean-field theory in multi-target tracking, IEEE Transactions on Automatic Control, 40 (6), 1070–1074.

    Article  MathSciNet  MATH  Google Scholar 

  71. Kastella, K. (1993) A maximum likelihood estimator for report-to-track association, Proceedings of the 1993 SPIE Conference on OE/Aerospace and Remote Sensing: Signal and Data Processing of Small Targets, SPIE Proc. Vol. 1954, 386–393.

    Google Scholar 

  72. Kastella, K., and Lutes, C. (1993) Coherent maximum likelihood estimation and mean-field theory in multi-target tracking, Proceedings of the Sixth Joint Service Data Fusion Symposium, Vol. I (Part 2), Johns Hopkins Applied Physics Laboratory, Laurel, Maryland, June 14–18, 1993, 971–982.

    Google Scholar 

  73. Krishnamurthy, V., and Evans, R. J. (1995) The data association problem for hidden Markov models, Proceedings of the 34th IEEE Conference on Decision and Control, Dec. 1995, New Orleans, 2764–2765.

    Google Scholar 

  74. Kruse, R., and Meyer, K. D. (1987) Statistics With Vague Data, D. Reidel/Kluwer, Dordrecht.

    Google Scholar 

  75. Kruse, R., Schwencke, E., and Heinsohn, J. (1991) Uncertainty and Vagueness in Knowledge-Based Systems, Springer-Verlag, New York.

    Book  MATH  Google Scholar 

  76. Lang, S. (1971) Algebra, Addison-Wesley, London.

    Google Scholar 

  77. Lanterman, A. D., Miller, M. I., Snyder, D. L., and Miceli, W. J. (1994) Jump-diffusion processes for the automated understanding of FLIR scenes, SPIE Proceedings, 2234, 416–427.

    Article  Google Scholar 

  78. Lewis, D. Probabilities of conditionals and conditional probabilities, Philosophical Review, 85, 297–315.

    Google Scholar 

  79. Lanterman, A.D., Miller, M.I., Snyder, D.L., and Miceli, W.J. (1994) Jump-diffusion processes for the automated understanding of FLIR scenes, SPIE Proceedings, Vol. 2234, 1994, 416–427

    Article  Google Scholar 

  80. Lewis, D. (1976) Probabilities of conditionals and conditional probabilities, Philosophical Review, 85, 297–315

    Article  Google Scholar 

  81. Li, Y. (1994) Probabilistic Interpretations of Fuzzy Sets and Systems, Doctoral Dissertation, Dept. of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, July 1994.

    Google Scholar 

  82. Lobbia, R. and Kent, M. (1994) Data fusion of decentralized local tracker outputs,“ IEEE Transactions on Aerospace and Electronic Systems, 30, 787–798.

    Article  Google Scholar 

  83. Lyshenko, N. N. (1983) Statistics of random compact sets in Euclidean space, Journal of Soviet Mathematics, 21, 76–92.

    Article  Google Scholar 

  84. Mahler, R. P. S. (1986) Combining ambiguous evidence with respect to ambiguous a priori Knowledge, I: Boolean Logic, IEEE Transactions on Systems, Man and Cybernetics—Part A: Systems and Humans, 26, 27–41.

    Article  Google Scholar 

  85. Mahler, R. P. S. (1994) Global integrated data fusion, Proceedings of the Seventh National Symposium on Sensor Fusion, Vol. I ( Unclassified), Sandia National Laboratories, Albuquerque, ERIM, Ann Arbor MI, pp. 187–199.

    Google Scholar 

  86. Mahler, R. P. S. (1996) Global optimal sensor allocation, Proceedings of the Ninth National Symposium on Sensor Fusion, Vol. I (Unclassified), Naval Postgraduate School, Monterey CA, March 11-13, 1996, 347–366.

    Google Scholar 

  87. Mahler, R. (1995) Information theory and data fusion, Proceedings of the Eighth National Symposium on Sensor Fusion, Vol. I (Unclassified), Texas Instruments, Dallas, Mar. 17–19 1995, ERIM, Ann Arbor MI, 279–292.

    Google Scholar 

  88. Mahler, R. (1995) Nonadditive probablity, finite-set statistics, and information fusion, Proceedings of the 34th IEEE Conference on Decision and Control, New Orleans, Dec. 1995, 1947–1952.

    Google Scholar 

  89. Mahler, R. (1994) The random-set approach to data fusion, SPIE Proceedings, 2234, 287–295.

    Article  Google Scholar 

  90. Mahler, R. (1996) Representing rules as random sets, I: Statistical Correlations Between Rules, Information Sciences, 88, 47–68.

    Article  MathSciNet  Google Scholar 

  91. Mahler, R. (1996) Representing rules as random sets, II: Iterated rules, International Journal of Intelligent Systems, 11, 583–610.

    Article  MATH  Google Scholar 

  92. Mahler (1996) Unified data fusion: fuzzy logic, evidence, and rules, SPIE Proceedings, 2755, 226–237.

    Google Scholar 

  93. Mahler, R. (1995) Unified nonparametric data fusion, SPIE Proceedings, 2484, 66–74.

    Article  Google Scholar 

  94. Matheron, G. (1975) Random Sets and Integral Geometry, J. Wiley, New York.

    Google Scholar 

  95. McGee, V. (1989) Conditional probabilities and compounds of conditionals, The Philosophical Review, 98 (4), 485–541.

    Article  MathSciNet  Google Scholar 

  96. Molchanov, I. S. (1993) Limit Theorems for Unions of Random Closed Sets, Springer-Verlag Lecture Notes in Mathematics No. 1561, New York.

    Google Scholar 

  97. Mori, S., Chong, C.-Y., Tse, E., and Wishner, R. P. (1984) Multitarget multisensor tracking problems, Part I: A general solution and a unified view on Bayesian approaches, Revised Version, Advanced Information and Decision Systems report TR-1048–01, Mountain View CA, August 1984. My thanks to Dr. Mori for making this report available to me (Dr. Shozo Mori, personal communication, Feb. 28 1995 ).

    Google Scholar 

  98. Mori, S., Chong, C. Y., Tse, E., and Wishner, R. P. (1986) Tracking and classifying multiple targets without a priori identification, IEEE Transactions on Automatic Control, AC-31, 401–409.

    Google Scholar 

  99. Muder, D. J. and O’Neil, S. D. (1993) The multi-dimensional SME filter for multitarget tracking, SPIE Proceedings, 1954, 587–599.

    Article  Google Scholar 

  100. Musicki, D., Evans, R., and Stankovic, S. (1994) Integrated probabilistic data association, IEEE Transactions on Automatic Control, 39, 1237–1241.

    Article  MathSciNet  MATH  Google Scholar 

  101. Neapolitan, R.E. (1992) A survey of uncertain and approximate inference, in L. Zadeh, J. Kocprzyk (eds.), Fuzzy Logic for the Management of Uncertainty, J. Wiley, New York.

    Google Scholar 

  102. Nguyen, H. T. and Walker, E. A. (1994) A history and introduction to the algebra of conditional events, IEEE Transactions on Systems, Man and Cybernetics, 24, 1671–1675.

    Article  MathSciNet  Google Scholar 

  103. Nguyen, H. T. and Walker, E. A. (1996), A First Course in Fuzzy Logic, CRC Press, Boca Raton, Florida.

    Google Scholar 

  104. Nguyen, H. T. (1978) On random sets and belief functions,Journal of Mathematical Analysis and Applications, 65, 531–542.

    MATH  Google Scholar 

  105. O’Neil, S. D. and Bridgland, M. F. (1991) Fast algorithms for joint probabilistic data association, Proceedings of the Fourth National Symposium on Sensor Fusion, Vol. I ( Unclassified), Orlando, ERIM, Ann Arbor MI, pp. 173–189.

    Google Scholar 

  106. Orlov, A. I. (1977) Relationships between fuzzy and random sets: fuzzy tolerances, Issledovania po Veroyatnostnostatishesk. Modelirovaniu Realnikh System, Moscow.

    Google Scholar 

  107. Orlov, A. I. (1978) Fuzzy and random sets, Prikladnoi Mnogomerni Statisticheskii Analys, Moscow.

    Google Scholar 

  108. Pawlak, Z. (1991) Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Dordrecht.

    MATH  Google Scholar 

  109. Poore, A. B., Robertson III, A. J., and Shea, P. J. (1995) A new class of Lagrangian relaxation based algorithms for fast data association in multiple hypothesis tracking applications, SPIE Proceedings, 2484, 184194.

    Google Scholar 

  110. Quinio, P. and Matsuyama, T. (1991) Random closed sets: a unified approach to the representation of imprecision and uncertainty, in R. Kruse and P. Siegel (eds.), Symbolic and Quantitative Approaches to Uncertainty, Springer-Verlag, New York, pp. 282–286.

    Chapter  Google Scholar 

  111. Reid, R. D. (1979) An algorithm for tracking multiple targets, IEEE Transactions on Automatic Control, AC-24, 843–854.

    Google Scholar 

  112. Revuz, A. (1956) Fonctions croissantes et mesures sur les espaces topologiques ordonnes, Ann. Inst. Fourier, VI, 187–268.

    Article  MathSciNet  Google Scholar 

  113. Sadjadi, F.A. (ed.) (1996) Selected Papers on Sensor and Data Fusion, SPIE Vol. MS-124.

    Google Scholar 

  114. Schay, G. (1968) An algebra of conditional events, Journal of Mathematical Analysis and Applications, 24, 334–344.

    Article  MathSciNet  MATH  Google Scholar 

  115. Schweizer, B. and Sklar, A. (1983) Probabilistic Metric Spaces, North-Holland, Amsterdam.

    MATH  Google Scholar 

  116. Singer, R. A., Sea, R. G., and Housewright, R. B. (1974) Derivation and evaluation of improved tracking filters for use in dense multi-target environments, IEEE Transactions on Information Theory, IT- 20, 423–432.

    Google Scholar 

  117. Skowron, A. (1989) The relationship between the rough set theory and evidence theory, Bulletin of the Polish Academy of Sciences: Technical Sciences, 37 (1-2), 87–90.

    Google Scholar 

  118. Smets, P. (1991) About updating, in B. D’Ambrosio, Ph. Smets, and P.P. Bonissone (eds.), Uncertainty in Artificial Intelligence, Morgan Kaufmann, pp. 378–385.

    Google Scholar 

  119. Smets, P. (1990) Constructing the pignistic probability function in a context of uncertainty, Uncertainty in Artificial Intelligence, 5, 29–39.

    Google Scholar 

  120. Smets, P. and Kennes, R. (1994) The transferable belief model, Artificial Intelligence, 66, 191–234.

    Article  MathSciNet  MATH  Google Scholar 

  121. Smets, P. (1992) The Transferable belief model and random sets, International Journal of Intelligent Systems, 7, 37–46.

    Article  MATH  Google Scholar 

  122. Sorenson, H. W., and Alspach, D. L. (1971) Recursive Bayesian estimation using Gaussian sums, Automatica, 7, 465–479.

    Article  MathSciNet  MATH  Google Scholar 

  123. Stein, M. C. (1995) Recursive Bayesian fusion for force estimation, Proc. 8th Nat’l Symp. on Sensor Fusion, Vol. I ( Unclassified ), Dallas TX, pp. 47–66.

    Google Scholar 

  124. Stone, L. D. Finn, M. V., and Barlow, C. A. (1996) Unified data fusion, Metron Corp. report to Office of Naval Research (ONR) under contract N00014–95-C-0052, Jan. 26 1996

    Google Scholar 

  125. Stoyan, D., Kendall, W. S., and Mecke, J. (1995) Stochastic Geometry and Its Applications, Second Edition, J. Wiley, New York.

    MATH  Google Scholar 

  126. Suppes, P. and Zanotti, M. (1977) On using random relations to generate upper and lower Probabilities, Syntheses, 36, 427–440.

    Article  MathSciNet  MATH  Google Scholar 

  127. Thoma, H. M. (1991) Belief function computations, in I.R. Goodman, M.M. Gupta, H.T. Nguyen and G.S. Rogers (eds.), Conditional Logic in Expert Systems, North-Holland, pp. 309–344.

    Google Scholar 

  128. Thoma, H. M. (1990) Factorization of Belief Functions, Doctoral Dissertation, Harvard University.

    Google Scholar 

  129. Thomopoulos, S. C. A. (1990) Sensor integration and data fusion, Journal of Robotic Systems, 7, 337–372.

    Article  Google Scholar 

  130. Tsaknakis, H., Buckley, M. D., and Washburn, R. B. (1991) Tracking closely-spaced objects using multi-assignment algorithms, Proceedings of the 1991 Joint Services Data Fusion Symposium, Vol. I (Unclassified), Johns Hopkins APL, Laurel MD, Naval Air Development Center, Warminster PA, pp. 293–309.

    Google Scholar 

  131. Uhlmann, J. K. (1992) Algorithms for multiple-target tracking, American Scientist, 80, 128–141.

    Google Scholar 

  132. van Fraassen, B.C. (1976) Probabilities of conditionals, in W.L. Harper and E.A. Hooker (eds.), Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science, Vol. I, D. Reidel, pp. 261–308.

    Google Scholar 

  133. Waltz, E., and Llinas, J. (1990) Multisensor Data Fusion, Artech House, Dedham, MA.

    Google Scholar 

  134. Washburn, R. B. (1987) A random point process approach to multiobject tracking, Proceedings of the American Control Conference, 3, 1846–1852.

    Google Scholar 

  135. Willett, P., Alford, M., and Vannicola, V. (1994) The case for like-sensor predetection fusion, IEEE Transactions on Aerospace and Electronic Systems, 30 (4), 986–1000.

    Article  Google Scholar 

  136. Xie, X. and Evans, R. J. (1991) Multiple target tracking and multiple frequency line tracking using hidden Markov models, IEEE Transactions on Signal Processing, 39, 2659–2676.

    Article  MATH  Google Scholar 

  137. Yen, J. (1986) A reasoning model based on an extended Dempster-Shafer theory, Proceedings of the AAAI-86 Conference, Philadephia PA, pp. 125–131.

    Google Scholar 

  138. Kadar, I. Abrams, B., Louas, L., Alford, M., Berry, W., and Liggins, M. (1994) Robust centralized predetection fusion for enhanced target detection, in Signal Processing, Sensor Fusion and Target Recognition III (I. Kadar and Z. Libby, Eds. ), Proceedings S.P.I.E. 2232, pp. 37–55.

    Google Scholar 

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Authors and Affiliations

  1. NCCOSC RDTE DIV, San Diego, California, USA

    I. R. Goodman

  2. Lockheed Martin Tactical Defences Systems, Saint Paul, Minnesota, USA

    Ronald P. S. Mahler

  3. Department of Mathematical Sciences, New Mexico State University, Las Cruces, New Mexico, USA

    Hung T. Nguyen

Authors
  1. I. R. Goodman
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  2. Ronald P. S. Mahler
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  3. Hung T. Nguyen
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© 1997 Springer Science+Business Media Dordrecht

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Goodman, I.R., Mahler, R.P.S., Nguyen, H.T. (1997). Data Fusion and Standard Techniques. In: Mathematics of Data Fusion. Theory and Decision Library, vol 37. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8929-1_2

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  • DOI: https://doi.org/10.1007/978-94-015-8929-1_2

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-4887-5

  • Online ISBN: 978-94-015-8929-1

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