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

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

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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.


    1. 1)
    2. 2)
      • Stuff, M.A.: `Three-dimensional analysis of moving target radar signals: methods and implications for ATR and feature-aided tracking', Proc. SPIE Int. Soc. Opt. Eng., 1999, 3721, Orlando, FL, p. 485–496.
    3. 3)
      • Jacobs, S.P., O'Sullivan, J.A.: `High-resolution radar model for joint tracking and recognition', Proc. IEEE National Radar Conf., May 1997, p. 99–104.
    4. 4)
      • O'Sullivan, J.A., Jacobs, S.P., Miller, M.I., Snyder, D.L.: `A likelihood-based approach to joint target tracking and identification', Proc. 27th Asilomar Conf. on Signals, Systems, and Computers, November 1993, 1, p. 290–294.
    5. 5)
    6. 6)
    7. 7)
      • E. Blasch , L. Hong . Sensor fusion cognition using belief filtering for tracking and identification. Proc. SPIE Int. Soc. Opt. Eng. , 250 - 259
    8. 8)
      • Blasch, E., Hong, L.: `Simultaneous feature-based identification and track fusion', Proc. of IEEE Int. Conf. on Decision and Control, Tampa, FL, December 1998, p. 239–244.
    9. 9)
      • B. Gu , L. Hong . Tracking 2-D rigid targets with invariant constraint. Inf. Sci. , 79 - 97
    10. 10)
    11. 11)
      • L. Hong , S. Wu , J. Layne . Invariant-based probabilistic target tracking and identification with GMTI/HRR measurements. IEE Proc., Radar, Sonar Nav.
    12. 12)
      • L. Hong , N. Cui , M. Pronobis , S. Scott . Local motion feature-aided ground moving-target tracking with GMTI and HRR measurements. IEEE Trans. Autom. Control
    13. 13)
      • Y. Bar-Shalom , T.E. Fortmann . (1988) Tracking and data association.

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