Using Group Knowledge for Multitarget Terrain-Based State Estimation

  • Edward Sobiesk
  • Maria Gini
  • John A. Marin
Conference paper


Multitarget terrain-based tracking is a cyclic process that combines sensor information with state estimation and data association techniques to maintain an estimate of the state of an environment in which ground-based vehicles are operating. When the ground-based vehicles are military vehicles moving across terrain, most of them will being moving in groups instead of autonomously. This work presents a methodology that has been demonstrated to improve the estimation aspect of the tracking process for this military domain. A clustering algorithm identifies groups within a vehicular data set. Group characteristics are extracted and then a new, innovative technique is utilized to integrate these into the individual vehicles’ state estimation process. A series of experiments shows that the proposed methodology significantly improves the performance of three classic estimation algorithms for multitarget terrain-based tracking.


Kalman Filter Statistical Improvement Group Knowledge Individual Vehicle Lead Vehicle 
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  1. 2.
    E. Brookner. Tracking and Kaiman Filtering Made Easy. Wiley, 1998.Google Scholar
  2. 3.
    R. Brown and P. Hwang. Introduction to Random Signal Analysis and Kaiman Filtering. John Wiley & Sons, Inc., 1997.Google Scholar
  3. 4.
    A. Doucet, N. de Freitas, and N. Gordon, editors. Sequential Monte Carlo Methods in Practice. Springer Verlag, 2000.Google Scholar
  4. 5.
    P. Nougues. Terrain data files from Fort Hood, 1994. Terrain data files in raster format created at the Systems Engineering Dept, University of Virginia.Google Scholar
  5. 6.
    P. Nougues. Tracking Intelligent Objects in Terrain. PhD thesis, University of Virginia, 1996.Google Scholar
  6. 7.
    B. Pitman and D. Tenne. Tracking a convoy of ground vehicles. Technical report, State University of New York at Buffalo, January 2002.Google Scholar
  7. 8.
    D. Reid and R. Bryson. A non-Gaussian filter for tracking targets moving over terrain. In 12th Asilomar Conf. on Circuits, Systems, and Computers, 1978.Google Scholar
  8. 9.
    H. Sidenbladh and S. Wirkander. Tracking random sets of vehicles in terrain. In Proc. 2nd IEEE Workshop on Multi-Object Tracking, 2003.Google Scholar
  9. 10.
    Edward J. Sobiesk. Using Group Knowledge to track multiple vehicles moving across terrain. PhD thesis, University of Minnesota, 2000.Google Scholar
  10. 11.
    S. Thrun, D. Fox, W. Burgard, and F. Dellaert. Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 128(1–2):99–141, 2001.MATHCrossRefGoogle Scholar
  11. 12.
    P. Yin and L. Chen. A new non-iterative approach for clustering. Pattern Recognition Letters, 15:125–133, 1994.MATHCrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Edward Sobiesk
    • 1
  • Maria Gini
    • 2
  • John A. Marin
    • 3
  1. 1.Dept of Electrical Engineering and Computer ScienceUnited States Military AcademyWest Point
  2. 2.Dept of Computer Science and EngineeringUniversity of MinnesotaMinneapolis
  3. 3.BBN TechnologiesColumbia

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