Optimal Placement of Wireless Sensor Networks for 2-Dimensional Source Localization

  • Yueqian LiangEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)


Optimal sensor network configuration for 2-Dimensional (2D) source localization is investigated systematically in this paper. The maximization of the determinant of Fisher information matrix (FIM) is chosen as the optimality criterion. Homogeneous range, received signal strength (RSS), time-of-arrival (TOA) and angle-of-arrival (AOA) sensor networks with different measurement noises are considered. The optimal configuration conditions for these four types of sensors are given out. Discussions based on these conditions are done to derive the optimal sensor configurations.


Optimal sensor configuration Sensor network Source localization Fisher information 


  1. 1.
    J.R. Lowell, Military applications of localization, tracking, and targeting. IEEE Wirel. Commun. 18(2), 60–65 (2011)CrossRefGoogle Scholar
  2. 2.
    J. Rantakokko, P. Händel, M. Fredholm, F. Marsten-Eklöf, User requirements for localization and tracking technology: a survey of mission-specific needs and constraints, in Proceedings of International Conference on Indoor Positioning and Indoor Navigation (IPIN) (Zürich, Switzerland, 2010), pp. 1–9Google Scholar
  3. 3.
    A.N. Bishop, B. Fidan, B.D. Anderson, K. Doğançay, P.N. Pathirana, Optimality analysis of sensor-target localization geometries. Automatica 46(3), 479–492 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    G. Marani, S.K. Choi, Underwater target localization: autonomous intervention with the DIDSON sonar in SAUVIM. IEEE Rob. Autom. Mag. 17(1), 64–70 (2010)CrossRefGoogle Scholar
  5. 5.
    J. Zhong, Z. Lin, Z. Chen, W. Xu, Cooperative localization using angle-of-arrival information, in Proceedings of 2014 11th IEEE International Conference on Control & Automation (ICCA) (Taichung, Taiwan, 2014), pp. 19–24Google Scholar
  6. 6.
    E. Xu, Z. Ding, S. Dasgupta, Source localization in wireless sensor networks from signal time-of-arrival measurements. IEEE Trans. Signal Process. 59(6), 2887–2897 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    X. Li, Collaborative localization with received-signal strength in wireless sensor networks. IEEE Trans. Veh. Technol. 56(6), 3807–3817 (2007)CrossRefGoogle Scholar
  8. 8.
    A.N. Bishop, P. Jensfelt, An optimality analysis of sensor-target geometries for signal strength based localization, in Proceedings of the 3rd International Conference on Intelligent Sensors, Sensor Networks, and Information Processing (Melbourne, Australia, Dec 2009), pp. 127–132Google Scholar
  9. 9.
    M.L. Hernandez, Optimal sensor trajectories in bearings-only tracking, in Proceedings of the 7th International Conference on Information Fusion (Stockholm, Sweden, 28-July 1 2004), pp. 893–900Google Scholar
  10. 10.
    K. Doğançay, Single- and multi-platform constrained sensor path optimization for angle-of-arrival target tracking, in Proceedings of the 18th European Signal Processing Conference (EUSIPCO-2010) (Aalborg, Denmark, Aug 2010), pp. 835–839Google Scholar
  11. 11.
    Y. Jia, Robust control with decoupling performance for steering and traction of 4WS vehicles under velocity-varying motion. IEEE Trans. Control Syst. Technol. 8(3), 554–569 (2000)CrossRefGoogle Scholar
  12. 12.
    Y. Jia, Alternative proofs for improved LMI representations for the analysis and the design of continuous-time systems with polytopic type uncertainty: a predictive approach. IEEE Trans. Autom. Control 48(8), 1413–1416 (2003)MathSciNetCrossRefGoogle Scholar
  13. 13.
    A.P. Aguiar, J.P. Hespanha, Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty. IEEE Trans. Autom. Control 52(8), 1362–1379 (2007)MathSciNetCrossRefGoogle Scholar
  14. 14.
    P.B. Sujit, S. Saripalli, J.B. Sousa, Unmanned aerial vehicle path following: a survey and analysis of algorithms for fixed-wing unmanned aerial vehicles. IEEE Control Syst. Mag. 42(1), 42–59 (2014)MathSciNetGoogle Scholar
  15. 15.
    S.R. Sukumar, H. Bozdogan, D.L. Page, A.F. Koschan, M.A. Abidi, Uncertainty minimization in multi-sensor localization systems using model selection theory, in Proceedings of the 19th International Conference on Pattern Recognition (Tampa, FL, USA, 2008), pp. 1–4Google Scholar
  16. 16.
    S.R. Semper, J.L. Crassidis, Decentralized geolocation and optimal path planning using limited UAVs, in Proceedings of the 12th International Conference on Information Fusion (Seattle, WA, USA, July 2009), pp. 355–362Google Scholar
  17. 17.
    S. Martínez, F. Bullo, Optimal sensor placement and motion coordination for target tracking. Automatica 42(4), 661–668 (2006)MathSciNetCrossRefGoogle Scholar
  18. 18.
    K. Doğançay, H. Hmam, Optimal angular sensor separation for AOA localization. Signal Process. 88(5), 1248–1260 (2008)CrossRefGoogle Scholar
  19. 19.
    K. Doğançay, H. Hmam, On optimal sensor placement for time-difference-of-arrival localization utilizing uncertainty minimization, in Proceedings of the 17th European Signal Processing Conference (EUSIPCO 2009) (Glasgow, Scotland, UK, 24–28 Aug 2009), pp. 1136–1140Google Scholar
  20. 20.
    S. Zhao, B.M. Chen, T.H. Lee, Optimal sensor placement for target localisation and tracking in 2D and 3D. Int. J. Control 86(10), 1687–1704 (2013)MathSciNetCrossRefGoogle Scholar
  21. 21.
    R. Larson, B.H. Edwards, Calculus, 9th edn. (Brooks/Cole, Belmont, CA, USA, 2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.China Academy of Electronics and Information TechnologyBeijingChina

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