Computational methods for processing ground penetrating radar data

  • Nurul Jihan Farhah Bostanudin

    Student thesis: Doctoral Thesis

    Abstract

    The aim of this work was to investigate signal processing and analysis
    techniques for Ground Penetrating Radar (GPR) and its use in civil
    engineering and construction industry. GPR is the general term applied to
    techniques which employ radio waves, typically in the Mega Hertz and Giga
    Hertz range, to map structures and features buried in the ground or in manmade
    structures. GPR measurements can suffer from large amount of noise.
    This is primarily caused by interference from other radio-wave-emitting
    devices (e.g., cell phones, radios, etc.) that are present in the surrounding
    area of the GPR system during data collection. In addition to noise, presence
    of clutter – reflections from other non-target objects buried underground in
    the vicinity of the target can make GPR measurement difficult to understand
    and interpret, even for the skilled human, GPR analysts.

    This thesis is concerned with the improvements and processes that can
    be applied to GPR data in order to enhance target detection and
    characterisation process particularly with multivariate signal processing
    techniques. Those primarily include Principal Component Analysis (PCA)
    and Independent Component Analysis (ICA). Both techniques have been
    investigated, implemented and compared regarding their abilities to separate
    the target originating signals from the noise and clutter type signals present in the data. Combination of PCA and ICA (SVDPICA) and two-dimensional
    PCA (2DPCA) are the specific approaches adopted and further developed in
    this work. Ability of those methods to reduce the amount of clutter and
    unwanted signals present in GPR data have been investigated and reported
    in this thesis, suggesting that their use in automated analysis of GPR images
    is a possibility.

    Further analysis carried out in this work concentrated on analysing the
    performance of developed multivariate signal processing techniques and at
    the same time investigating the possibility of identifying and characterising
    the features of interest in pre-processed GPR images. The driving idea
    behind this part of work was to extract the resonant modes present in the
    individual traces of each GPR image and to use properties of those poles to
    characterise target. Three related but different methods have been
    implemented and applied in this work – Extended Prony, Linear Prediction
    Singular Value Decomposition and Matrix Pencil methods. In addition to
    these approaches, PCA technique has been used to reduce dimensionality of
    extracted traces and to compare signals measured in various experimental
    setups. Performance analysis shows that Matrix Pencil offers the best
    results.
    Date of AwardMay 2013
    Original languageEnglish
    SupervisorBranislav Vuksanovic (Supervisor), Alan Hewitt (Supervisor) & Boris Gremont (Supervisor)

    Cite this

    '