Journal of Systems Science and Complexity

, Volume 27, Issue 3, pp 413–429 | Cite as

Cooperative shift estimation of target trajectory using clustered sensors

  • Jiangping HuEmail author
  • Xiaoming Hu
  • Tielong Shen


In this paper, a mathematical model for target tracking using nonlinear scalar range sensors is formulated first. A time-shift sensor scheduling strategy is addressed on the basis of a k-barrier coverage protocol and all the sensors are divided into two classes of clusters, active cluster, and submissive cluster, for energy-saving. Then two types of time-shift nonlinear filters are proposed for both active and submissive clusters to estimate the trajectory of the moving target with disturbed dynamics. The stochastic stability of the two filters is analyzed. Finally, some numerical simulations are given to demonstrate the effectiveness of the new filters with a comparison of EKF.

Key words

Cluster sensor network target tracking time-shift estimation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Akyildiz I F, Su W L, Sankarasubramaniam Y, and Cayirci E, A survey on sensor networks, IEEE Communications Magazine, 2002, 40(8): 102–114.CrossRefGoogle Scholar
  2. [2]
    Brooks R, Ramanathan P, and Sayeed A M, Distributed target classification and tracking in sensor networks, Proc. of IEEE, 2003, 91(8): 1163–1171.CrossRefGoogle Scholar
  3. [3]
    Kumar S, Zhao F, and Shepherd D, Collaborative signal and information processing in microsensor networks, IEEE Signal Processing Magazine, 2002, 19(2): 13–14.CrossRefzbMATHGoogle Scholar
  4. [4]
    Zhao F, Shin J, and Reich J, Information-driven dynamic sensor collaboration for tracking applications, IEEE Signal Processing Magzine, 2002, 19(2): 61–72.CrossRefGoogle Scholar
  5. [5]
    Brooks R, Griffn C, and Friedlander D, Self-organized distributed sensor network entity tracking, Int. J. of High Performance Comput. Application, 2002, 16(3): 207–219.CrossRefGoogle Scholar
  6. [6]
    Jazwinski A H, Stochastic Process and Filtering Theory, Dover Publications, 2007.Google Scholar
  7. [7]
    Julier S, Uhlmann J, and Durrant-Whyte H, A new approach for filtering nonlinear system, Proc. of the American Control Conference, Washington, DC, 1995, 1628–1632.Google Scholar
  8. [8]
    Doucet A, Freitas N D, and Gordon N J, Sequential Monte Carlo Methods in Practice, Springer, 2001.CrossRefzbMATHGoogle Scholar
  9. [9]
    Reif K, Gunther S, Yaz E, and Unbehauen R, Stochastic stability of the continuous-time extended Kalman filter, IEE Proc. Contr. Theory and Appl., 2000, 147(1): 45–52.CrossRefGoogle Scholar
  10. [10]
    Hu J and Hu X, Optimal target trajectory estimation and filtering using networked sensors, Journal of Systems Science and Complexity, 2008, 21(3): 325–336.CrossRefzbMATHMathSciNetGoogle Scholar
  11. [11]
    Hu J and Hu X, Nonlinear filtering in target tracking using cooperative mobile sensors, Automatica, 2010, 46(12): 2041–2046.CrossRefzbMATHMathSciNetGoogle Scholar
  12. [12]
    He Y and Chong E, Sensor scheduling for target tracking in sensor networks, Proc. of the 43rd IEEE CDC, Atlantis, Bahamas, 2004, 743–748.Google Scholar
  13. [13]
    Atia G K, Veeravalli V, and Fuemmeler J A, Sensor scheduling for energy-efficient target tracking in sensor networks, IEEE Trans. on Signal Processing, 2011, 59(10): 4923–4937.CrossRefMathSciNetGoogle Scholar
  14. [14]
    Kumar S, Lai T H, Posner M E, and Sinha P, Optimal sleep-wakeup algorithms for barriers of wireless wensors, Proc. of the 4th International Conference on Broadband Communications, Networks and Systems (BROADNETS), 2007, 327–336.Google Scholar
  15. [15]
    Ammari H M and Das S K, Centralized and custered k-coverage protocols for wireless sensor networks, IEEE Trans. on Computers, 2012, 61(1): 118–133.CrossRefMathSciNetGoogle Scholar
  16. [16]
    Kumar S, Lai T H, and Arora A, Barrier coverage with wireless sensors, Proc. of the 11th Annual International Conference on Mobile Computing and Networking (ACM MobiCom), 2005, 284–298.Google Scholar
  17. [17]
    Zhang H and Hou J C, Is deterministic deployment worse than random deployment for wireless sensor networks?, Proc. of the 25th IEEE International Conference on Computer Communications (INFOCOM), Spain, 2006, 1–13.Google Scholar
  18. [18]
    Xing G, Wang X, Zhang Y, Lu C, Pless R, and Gill C, Integrated coverage and connectivity configuration for energy conservation in sensor networks, ACM Trans. Sensor Networks, 2005, 1(1): 36–72.CrossRefGoogle Scholar
  19. [19]
    Zakai M, On the ultimate boundedness of moments associated with solutions of stochastic differential equations, SIAM J. Control, 1967, 5(4): 588–593.CrossRefzbMATHMathSciNetGoogle Scholar
  20. [20]
    Cheng D, Ghosh B, and Hu X, Distributed sensor network for target tracking, Proc. of MTNS 2006, Kyoto, 2006, 2171–2183.Google Scholar
  21. [21]
    Olfati-Saber R and Murray R, Consensus problems in networks of agents with switching topology and time-delays, IEEE Trans. Automatic Control, 2004, 49(9): 1520–1533.CrossRefMathSciNetGoogle Scholar

Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Automation EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Optimization and Systems Theory and ACCESS Linnaeus CenterRoyal Institute of TechnologyStockholmSweden
  3. 3.Department of Engineering and Applied SciencesSophia UniversityTokyoJapan

Personalised recommendations