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Wireless Personal Communications

, Volume 109, Issue 4, pp 2107–2120 | Cite as

Neighbor Dependency-Based Dynamic Fusion Tree Generation for a Multi-radar System

  • Kyuoke YeunEmail author
  • Daeyoung Kim
Article
  • 22 Downloads

Abstract

This paper proposes a fusion tree generation (FTG) algorithm for a two-tier fusion process in a multi-radar system. The two-tier fusion process divides the fusion process into local and global parts. The fusion workload of a multi-radar system at the central server can be reduced by applying two-tier approach. However, the two-tier fusion process requires a balanced fusion tree to increase the number of processed tracks. This paper presents a dynamic fusion tree generation (D-FTG) algorithm based on a clustering scheme. We developed a novel clustering scheme, which can be used in generating balanced fusion trees in a multi-radar system. The developed clustering scheme is able to generate a balanced fusion tree with the neighbor dependency-based scoring method, manage the dynamic changes of the distribution of targets with the pruning-and-rejoining strategy, and cope with the failure of radar nodes by repeating initial clustering. Simulation results show that D-FTG outperforms existing clustering methods when used to generate balanced fusion trees in a dynamic multi-radar system.

Keywords

Air surveillance system Clustering Fusion tree Multi-sensor tracking Two-tier fusion process 

Notes

References

  1. 1.
    Liebowitz, J., Ayyavoo, N., Nguyen, H., Carran, D., & Simien, J. (2007). Cross-generational knowledge flows in edge organizations. Industrial Management and Data Systems,107, 1123–1153.  https://doi.org/10.1108/02635570710822787.CrossRefGoogle Scholar
  2. 2.
    Neil, W. D. O. & Mcnair, F. L. J. (2007). The cooperative engagement capability (CEC) transforming naval anti-air warfare. Case Studies in National Security Transformation. http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=ADA471258.
  3. 3.
    Kopp, C. (2003). Network centric warfare in the Land Environment. Defense TODAY Magazine, 8, 1–5.Google Scholar
  4. 4.
    Masinsin, R. Q. (2000). The single integrated air picture: Building synergy for theater air and missile defense?. Quantico, VA: Marine Corps Command and Staff College.Google Scholar
  5. 5.
    Martin, T. W., & Chang, K. C. (2005). A distributed data fusion approach for mobile ad hoc networks. In: 2005 7th international conference on information fusion (pp. 1062–1069).  https://doi.org/10.1109/icif.2005.1591975.
  6. 6.
    Yeun, K., Jun, T.J., & Kim, D. (2016). Distributed self-organized cluster-based fusion tree generation algorithm. In: 2016 International conference on control, decision and information technologies (CoDIT) (pp. 198–203).  https://doi.org/10.1109/codit.2016.7593560.
  7. 7.
    Yeun, K., & Kim, D. (2017). Non-uniform fusion tree generation in a dynamic multi-sensor system. Sensors (Switzerland),17, 1020.  https://doi.org/10.3390/s17051020.CrossRefGoogle Scholar
  8. 8.
    Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion,14, 28–44.  https://doi.org/10.1016/j.inffus.2011.08.001.CrossRefGoogle Scholar
  9. 9.
    Kim, Y., & Bang, H. (2015). Airborne multisensor management for multitarget tracking. In: 2015 International conference on unmanned aircraft systems (ICUAS) (pp. 751–756).  https://doi.org/10.1109/icuas.2015.7152358.
  10. 10.
    Cheng, T., & Chen, S. (2014). Flexible fusion structure for air task networks. In: 2014 International conference on multisensor fusion and information integration for intelligent systems (MFI) (pp. 1–6). IEEE.Google Scholar
  11. 11.
    Dimokas, N., Katsaros, D., & Manolopoulos, Y. (2010). Energy-efficient distributed clustering in wireless sensor networks. Journal of Parallel and Distributed Computing,70, 371–383.  https://doi.org/10.1016/j.jpdc.2009.08.007.CrossRefzbMATHGoogle Scholar
  12. 12.
    Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing,3, 366–379.  https://doi.org/10.1109/TMC.2006.141.CrossRefGoogle Scholar
  13. 13.
    Sohn, I., Lee, J.-H., & Lee, S. H. (2016). Low-energy adaptive clustering hierarchy using affinity propagation for wireless sensor networks. IEEE Communications Letters,20, 558–561.  https://doi.org/10.1109/LCOMM.2016.2517017.CrossRefGoogle Scholar
  14. 14.
    Dueck, D., & Frey, B. J. (2007). Clustering by passing messages between data points. Science,315, 972–976.  https://doi.org/10.1126/science.1136800.MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Nayyar, A., & Gupta, A. (2014). A comprehensive review of cluster-based energy efficient routing protocols in wireless sensor networks. IJRCCT,3, 104–110.Google Scholar
  16. 16.
    Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications,30, 2826–2841.  https://doi.org/10.1016/j.comcom.2007.05.024.CrossRefGoogle Scholar
  17. 17.
    Rossi, F., Van Beek, P., & Walsh, T. (2006). Handbook of constraint programming (Foundations of Artificial Intelligence). Amsterdam: Elsevier.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Agency for Defense DevelopmentDaejeonKorea
  2. 2.Department of Computer ScienceKAISTDaejeonKorea

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