• DocumentCode
    756428
  • Title

    Statistical method to detect subsurface objects using array ground-penetrating radar data

  • Author

    Xu, Xiaoyin ; Miller, Eric L. ; Rappaport, Carey M. ; Sower, Gary D.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA
  • Volume
    40
  • Issue
    4
  • fYear
    2002
  • fDate
    4/1/2002 12:00:00 AM
  • Firstpage
    963
  • Lastpage
    976
  • Abstract
    We introduce a combination of high-dimensional analysis of variance (HANOVA) and sequential probability ratio test (SPRT) to detect buried objects from an array ground-penetrating radar (GPR) surveying a region of interest in a progressive manner. Using HANOVA, we exploit the transient characteristic of GPR signals in the time domain to extract information about buried objects at fixed positions of the array. Based on the output of the HANOVA, the SPRT is employed to make detection decisions recursively as the array moves downtrack. The method is on-line implementable and of low computational complexity. Our approach is validated using field-data from two quite different GPR sensing systems designed for landmine detection applications
  • Keywords
    buried object detection; geophysical techniques; military systems; radar theory; remote sensing by radar; statistical analysis; terrain mapping; terrestrial electricity; HANOVA; buried object detection; geoelectric method; geophysical measurement technique; ground penetrating radar; high-dimensional analysis of variance; landmine; military system; mine detection; radar array; radar remote sensing; sequential probability ratio test; statistical method; subsurface object; terrain mapping; terrestrial electricity; transient characteristic; transients; Analysis of variance; Buried object detection; Computational complexity; Data mining; Ground penetrating radar; Object detection; Probability; Radar detection; Sequential analysis; Statistical analysis;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0196-2892
  • Type

    jour

  • DOI
    10.1109/TGRS.2002.1006391
  • Filename
    1006391