• DocumentCode
    1057762
  • Title

    2D rigid-body target modelling for tracking and identification with GMTI/HRR measurements

  • Author

    Wu, S. ; Hong, L. ; Layne, J.R.

  • Author_Institution
    Dept. of Electr. Eng., Wright State Univ., Dayton, OH, USA
  • Volume
    151
  • Issue
    4
  • fYear
    2004
  • fDate
    7/24/2004 12:00:00 AM
  • Firstpage
    429
  • Lastpage
    438
  • Abstract
    Joint ground moving-target tracking identification is a crucial task in a modern combat operation. Due to the entirely different environment, ground moving-target tracking is quite different from airborne target tracking. A major difference lies in target modelling. In airborne target tracking, a target is usually treated as a point, while for ground target tracking, a target is considered a rigid body. Two approaches for 2D rigid-body target modelling are proposed. Equipped with ground moving-target indicator and high-resolution range sensors, the new approaches effectively explore the concepts of local and global motions of a rigid body. The kinematics of a global motion is described by a constant acceleration model, and a local motion is modelled by the pivoting centre and pseudocentre approaches. The proposed models are implemented by the extended Kalman filter with and without a probabilistic data association filter. The simulation results show that the proposed approaches not only correctly track a rigid-body target in a complicated scenario but also simultaneously report its structural information.
  • Keywords
    Kalman filters; identification; military systems; sensors; target tracking; extended Kalman filter; ground moving-target indicator; high-resolution range sensors; moving-target tracking identification; pivoting centre; probabilistic data association filter; pseudocentre approach;
  • fLanguage
    English
  • Journal_Title
    Control Theory and Applications, IEE Proceedings -
  • Publisher
    iet
  • ISSN
    1350-2379
  • Type

    jour

  • DOI
    10.1049/ip-cta:20040694
  • Filename
    1322125