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Finite set statistics with applications to data fusion

  • Avner Friedman
Chapter
  • 154 Downloads
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 67)

Abstract

Data fusion is concerned with the following general problem: Locate and identify objects of many different types on the basis of different kinds of evidence. The evidence is collected on an ongoing basis by many sensors having varying capabilities. Some evidence might be (i) probabilistic, e.g., estimated location with Gaussian distribution supplied by radar, (ii) incomplete probabilistic, e.g., partially specified location distribution supplied by sonar, (iii) linguistic, e.g., sighting by pilot (“sub is probably in region A but may also have been in region B”), (iv) conditional, e.g., if A is observed then B is observed with probability p(BA),etc.

Keywords

False Alarm Probability Density Function Data Fusion Belief Measure Additive Probability Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1995

Authors and Affiliations

  • Avner Friedman
    • 1
  1. 1.Institute for Mathematics and its ApplicationsUniversity of MinnesotaMinneapolisUSA

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