Multiple Hypothesis Correlation in Track-to-Track Fusion Management

  • Aubrey B Poore
  • Sabino M Gadaleta
  • Benjamin J Slocumb
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 88)


Track to track fusion systems require a capability to perform track matching across the reporting sensors. In conditions where significant ambiguity exists, for example due to closely spaced objects, a simple single frame assignment algorithm can produce poor results. For measurement-to-track fusion this has long been recognized and sophisticated multiple hypothesis, multiple frame, data association methods considerably improve tracking performance in these challenging scenarios. The most successful of the multiple frame methods are multiple hypothesis tracking (MHT) and multiple frame assignments (MFA), which is formulated as a multidimensional assignment problem. The performance advantage of the multiple frame methods over the single frame methods follows from the ability to hold difficult decisions in abeyance until more information is available and the opportunity to change past decisions to improve current decisions. In this chapter, the multiple source track correlation and fusion problem is formulated as a multidimensional assignment problem. The computation of cost coefficients for the multiple frame correlation assignments is based on a novel batch MAP estimation approach. Based on the multidimensional assignments we introduce a novel multiple hypothesis track correlation approach that allows one to make robust track management decisions over multiple frames of data. The use of the proposed multiple hypothesis, multiple frame correlation system, is expected to improve the fusion system performance in scenarios where significant track assignment ambiguity exists. In the same way that multiple frame processing has shown improvements in the tracking performance in measurement-to-track fusion applications, we expect to achieve improvements in the track-to-track fusion problem.


Track fusion multiple hypothesis track correlation multidimensional assignment 


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  1. [Bar-Shalom, 1990]
    Bar-Shalom, Y., editor (1990). Multitarget-Multisensor Tracking: Advanced Applications. Artech House, Dedham, MA.Google Scholar
  2. [Bar-Shalom, 1992]
    Bar-Shalom, Y., editor (1992). Multitarget-Multisensor Tracking: Applications and Advances. Artech House, Dedham, MA.Google Scholar
  3. [Bar-Shalom and Blair, 2000]
    Bar-Shalom, Y. and Blair, W. D., editors (2000). Multitarget-Multisensor Tracking: Applications and Advances, Volume III. Artech House, Dedham, MA.Google Scholar
  4. [Bar-Shalom and Li, 1995]
    Bar-Shalom, Y. and Li, X.-R. (1995). Multitarget-Multisensor Tracking: Principles and Techniques. YBS.Google Scholar
  5. [Blackman and Popoli, 1999]
    Blackman, S. and Popoli, R. (1999). Design and Analysis of Modern Tracking Systems. Artech House, Norwood, MA.zbMATHGoogle Scholar
  6. [Chen et al., 2003]
    Chen, H., Kirubarajan, T., and Bar-Shalom, Y. (2003). Performance limits of track-to-track fusion versus centralized estimation: theory and application. IEEE Transactions on Aerospace and Electronic Systems, 39(2):386–398.CrossRefGoogle Scholar
  7. [Chong et al., 2000]
    Chong, C.-Y, Mori, S., Barker, W., and Chang, K.-C. (2000). Architectures and algorithms for track association and fusion. IEEE AES Systems Magazine, 15:5–13.CrossRefGoogle Scholar
  8. [Chong et al., 1990]
    Chong, C. Y, Mori, S., and Chang, K. C. (1990). Distributed multitarget multisensor tracking. In Bar-Shalom, Y, editor, Multitarget-Multisensor Tracking: Advanced Applications, volume 1, chapter 8, pages 247–295. Artech House, Norwood, MA.Google Scholar
  9. [Drummond, 1997]
    Drummond, O. E. (1997). A hybrid fusion algorithm architecture and tracklets. Signal and Data Processing of Small Targets 1997, SPIE, 3136:485–502.Google Scholar
  10. [Frenkel, 1995]
    Frenkel, G. (1995). Multisensor tracking of ballistic targets. In SPIE, volume 2561, pages 337–346.MathSciNetADSCrossRefGoogle Scholar
  11. [Gadaleta et al., 2002]
    Gadaleta, S., Klusman, M., Poore, A. B., and Slocumb, B. J. (2002). Multiple frame cluster tracking. In SPIE Vol. 4728, Signal and Data Processing of Small Targets, pages 275–289.Google Scholar
  12. [Gadaleta et al., 2004]
    Gadaleta, S., Poore, A. B., Roberts, S., and Slocumb, B. J. (2004). Multiple hypothesis clustering, multiple frame assignment tracking. In SPIE Vol. 5428, Signal and Data Processing of Small Targets.Google Scholar
  13. [Jazwinski, 1970]
    Jazwinski, A. (1970). Stochastic processes and filtering theory. Academic Press, New York.zbMATHGoogle Scholar
  14. [Liggens et al., 1997]
    Liggens, M. E., Chong, C. Y., Kadar, I., Alford, M. G., Vannicola, V., and Thomopoulos, S. (1997). Distributed fusion architectures and algorithms for target tracking. Proceedings of the IEEE, 85(l):95–107.CrossRefGoogle Scholar
  15. [Murty, 1968]
    Murty, K. (1968). An algorithm for ranking all the assignments in order of increasing cost. Operations Research, 16:682–687.zbMATHCrossRefGoogle Scholar
  16. [Poore and Gadaleta, 2003]
    Poore, A. and Gadaleta, S. (2003). The group assignment problem. In SPIE Vol. 5204, Signal and Data Processing of Small Targets, pages 595–607.Google Scholar
  17. [Poore, 1994]
    Poore, A. B. (1994). Multidimensional assignment formulation of data association problems arising from multitarget tracking and multi-sensor data fusion. Computational Optimization and Applications, 3:27–57.zbMATHMathSciNetCrossRefGoogle Scholar
  18. [Poore, 1995]
    Poore, A. B. (1995). Multidimensional assignments and multi-target tracking. In Cox, I. J., Hansen, P., and Julesz, B., editors, Partitioning Data Sets, volume 19 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science, pages 169–198, Providence, RI. American Mathematical Society.Google Scholar
  19. [Poore et al., 2001a]
    Poore, A. B., Lu, S., and Suchomel, B. (2001a). Network MFA tracking architectures. In Proceedings of SPIE Conference on Small Targets 2001, Oliver E. Drummond Editor.Google Scholar
  20. [Poore et al., 2001b]
    Poore, A. B., Lu, S., and Suchomel, B. J. (2001b). Data association using multiple frame assignments. In Handbook of Multisensor Data Fusion. CRC Press LLC.Google Scholar
  21. [Poore et al., 2003]
    Poore, A. B., Slocumb, B. J., Suchomel, B. J., Obermeyer, F. H., Herman, S. M., and Gadaleta, S. M. (2003). Batch Maximum Likelihood (ML) and Maximum A Posteriori (MAP) estimation with process noise for tracking applications. In SPIE Vol. 5204, Signal and Data Processing of Small Targets, pages 188–199.Google Scholar
  22. [Sage and Melsa, 1971]
    Sage, A. and Melsa, J. (1971). Estimation theory with applications to communications and control. McGraw-Hill, New York.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Aubrey B Poore
    • 1
    • 2
  • Sabino M Gadaleta
    • 2
  • Benjamin J Slocumb
    • 2
  1. 1.Department of MathematicsColorado State UniversityFort CollinsUSA
  2. 2.NumericaFort CollinsUSA

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