• DocumentCode
    3466449
  • Title

    A review of high-level multisensor fusion: approaches and applications

  • Author

    Luo, Ren C. ; Su, Kuo L.

  • Author_Institution
    Dept. of Electr. Eng., Nat. Chung Cheng Univ., Chia-Yi, Taiwan
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    25
  • Lastpage
    31
  • Abstract
    The potential advantages in multisensor fusion can be obtained more accurately, concerning feature that are impossible to perceive with individual sensors, as well as in less time, and at a lower cost. The characterization most commonly encountered in the rapidly growing multisensor fusion literature based on levels of detail in the information is that of the now well known triple low level (data level), medium level (feature level) and high level (decision level). The development of high-level multisensor fusion representations is very important, in the construction of advanced intelligent systems. The paper begins with a review on the fundamental principles about high-level multisensor fusion, together with some of the applications. Finally, we compare the decision algorithms in the high-level multisensor fusion
  • Keywords
    artificial intelligence; decision theory; information theory; probability; sensor fusion; statistical analysis; data level; decision level; decision theory; feature level; high-level representations; information theory; intelligent systems; multisensor fusion; probability; statistical analysis; Automation; Bayesian methods; Humans; Inference algorithms; Intelligent sensors; Intelligent systems; Laboratories; Sensor fusion; Sensor phenomena and characterization; Sensor systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multisensor Fusion and Integration for Intelligent Systems, 1999. MFI '99. Proceedings. 1999 IEEE/SICE/RSJ International Conference on
  • Conference_Location
    Taipei
  • Print_ISBN
    0-7803-5801-5
  • Type

    conf

  • DOI
    10.1109/MFI.1999.815960
  • Filename
    815960