• DocumentCode
    1600160
  • Title

    Statistical information fusion criteria for multi-sensory systems

  • Author

    Lin, Hong-Dar ; Chang, C. Alec

  • Author_Institution
    Dept. of Ind. Eng., Tunghai Univ., China
  • fYear
    1995
  • Firstpage
    535
  • Lastpage
    539
  • Abstract
    Most current information fusion techniques focus on adding all information sources, and then observe the operation of a fused system. These methods generally do not concern whether a new sensor source would enhance system performances beforehand. The statistical meta-analysis offers a set of quantitative techniques that permit synthesizing a variety of independent information sources. Using the statistical meta-analysis, this paper presents a method that can provide fusion criteria to foretell the effect of adding a new information source in terms of statistical type I errors before the source is actually combined. This method is then implemented as an illustration
  • Keywords
    sensor fusion; statistical analysis; information sources synthesis; multi-sensory systems; quantitative techniques; statistical information fusion criteria; statistical meta-analysis; statistical type I errors; Expert systems; Feedforward neural networks; Fuzzy logic; Industrial engineering; Neural networks; Production systems; Sensor fusion; Sensor systems; Statistical analysis; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Automation and Control: Emerging Technologies, 1995., International IEEE/IAS Conference on
  • Conference_Location
    Taipei
  • Print_ISBN
    0-7803-2645-8
  • Type

    conf

  • DOI
    10.1109/IACET.1995.527615
  • Filename
    527615