• DocumentCode
    1879622
  • Title

    Application of the Integrated Micro Acceleration Measurement System in Target Recognition

  • Author

    Lan, Jinhui ; Yin, Yixin ; Lan, Tian

  • Author_Institution
    Dept. of Meas. & Control Technol., Beijing Univ. of Sci. & Technol.
  • fYear
    2006
  • fDate
    24-27 April 2006
  • Firstpage
    498
  • Lastpage
    500
  • Abstract
    It is studied that an integrated micro acceleration measurement system was used to detect the seismic acceleration signals from moving vehicle targets and recognize these targets in the paper. The seismic signals of typical vehicles have been tested by the system and analyzed in this paper, because seismic properties of vehicle targets are an important index of target recognition. In order to realize the target classification and recognition, a technique of artificial neural networks combined with Dempster-Shafer theory of evidence (ANNCDSTE) is applied to classification of seismic signals. The technique and its architecture have been presented. Through outdoor experiments, it can be proven that seismic properties of target acquired by the micro acceleration measurement system are correct, ANNCDSTE method is effective to solve the problem of target recognition
  • Keywords
    accelerometers; microsensors; neural nets; seismic waves; signal classification; signal detection; target tracking; Dempster-Shafer evidence theory; artificial neural networks; integrated micro acceleration measurement system; moving vehicle targets; seismic acceleration signals; seismic signal classification; seismic signals; target classification; target recognition; Acceleration; Accelerometers; Artificial neural networks; Automotive engineering; Control systems; Paper technology; Signal processing; Target recognition; Testing; Vehicle detection; Acceleration measurement; MEMS; Target recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Instrumentation and Measurement Technology Conference, 2006. IMTC 2006. Proceedings of the IEEE
  • Conference_Location
    Sorrento
  • ISSN
    1091-5281
  • Print_ISBN
    0-7803-9359-7
  • Electronic_ISBN
    1091-5281
  • Type

    conf

  • DOI
    10.1109/IMTC.2006.328550
  • Filename
    4124376