Skip to main content
SpringerLink
Log in

Mobile Target Positioning Using Refining Distance Measurements with Inaccurate Anchor Nodes in Chain-Type Wireless Sensor Networks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

As a class of long and narrow structures widely exist such as the river, road, mine tunnel, pipe, chain-type wireless sensor networks (CWSN) can be applied to monitor these environments. The accurate position estimation is a key technology for the mobile target in CWSN. This paper proposes an innovative positioning method to estimate the position of mobile target. Firstly, wireless signals can be affected by measurement noises, coordinate errors of anchor nodes, and chain scene structure. Kernel canonical correlation analysis is applied to analyze the correlation coefficients of these nonlinear wireless signal sets. Secondly, we search out two maximum correlative sets of wireless signals and integrate them into a set of optimal wireless signals. Thirdly, the uncertainty coordinate of anchor node is modeled and the position of mobile target is estimated under measurement and geometry constraints. Furthermore, we simulate the proposed method for mobile target, in comparison with the weighted least squares (WLS) and CHAN methods. Estimation results indicate that the proposed method can refine distance measurement accuracy and perform better positioning performance than WLS and CHAN methods, when we vary the conditions of TDOA/AOA measurement errors, anchor nodes coordinate errors, and anchor nodes spacing distance. Finally, the actual positioning experiments are implemented in a corridor, which show that the practical estimation results are similar to the simulation results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Albano M, Hadzic S, Rodriguez J (2013) Use of negative information in positioning and tracking algorithms. Telecommun Syst 53(3):285–298

    Article  Google Scholar 

  2. Chen HY, Gao FF, Martins M, Huang P, Liang JL (2013) Accurate and efficient node localization for mobile sensor networks. Mob Netw Appl 18(1):141–147

    Article  Google Scholar 

  3. Liu ZG, Li CW, Wu DC, Geng S, Ding Q (2010) A wireless sensor network based personnel positioning scheme in coal mines with blind areas. Sensors 10(11):9891–9918

    Article  Google Scholar 

  4. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–114

    Article  Google Scholar 

  5. Stankunas J, Rudinskas D, Lasauskas E (2011) Experimental research of wireless sensor network application in aviation. Elektronika IR Elektrotechnika 5:41–44

    Google Scholar 

  6. Jo H, Sim S, Nagayama T (2012) Development and application of high-sensitivity wireless smart sensors for decentralized stochastic modal identification. J Eng Mech ASCE 138(6):683–694

    Article  Google Scholar 

  7. Zhu DR, Han JH, Ou J, Min J (2013) Single beacon cruise positioning algorithm in wireless sensor networks. Chin J Electron 22(3):558–562

    Google Scholar 

  8. He T, Huang CD, Blum BM, Stankovic JA, Abdelzaher T (2003) Range-free localization schemes for large scale sensor networks. In Proc. of the 9th Annual International Conference on Mobile Computing And Networking, New York, USA, Sep, pp. 81–95.

  9. Wang J, Prasad RV, An XL (2012) A study on wireless sensor network based indoor positioning systems for context-aware applications. Wirel Commun Mob Comput 12(1):53–70

    Article  Google Scholar 

  10. Chen HY, Liu B, Huang P, Liang JL, Gu Y (2012) Mobility-assisted node localization based on TOA measurements without time synchronization in wireless sensor networks. Mob Netw Appl 17(1):90–99

    Article  Google Scholar 

  11. Luo CM, Li W, Fan MB, Yang H, Fan QG (2014) A collaborative positioning algorithm for mobile target using multisensor data integration in enclosed environments. Comput Commun 44:26–35

    Article  Google Scholar 

  12. Wen CY, Chan FK (2010) Adaptive AOA-aided TDOA self-positioning for mobile wireless sensor network. Sensors 10(11):9742–9770

    Article  Google Scholar 

  13. Song H, Shin V, Jeon M (2012) Mobile node localization using fusion prediction-based interacting multiple model in cricket sensor network. IEEE Trans Ind Electron 59(11):4349–4359

    Article  Google Scholar 

  14. Via J, Santamaria I, Perez J (2007) A learning algorithm for adaptive canonical correlation analysis of several data sets. Neural Netw 20(1):139–152

    Article  MATH  Google Scholar 

  15. Chen CW, Wang Y (2008) Chain-type wireless sensor network for monitoring long range infrastructures: architecture and protocols. Int J Distrib Sens Netw 4(4):287–314

    Article  Google Scholar 

  16. Hotelling H (1936) Relation between two sets of variates. Biometrika 28:321–377

    Article  MATH  Google Scholar 

  17. Gu JJ, Chen SC, Zhuang Y (2010) Wireless sensor network-based topology structures for the internet of things localization. Chinese Journal of Computers 33(9):1548–1556

    Article  Google Scholar 

  18. Pan JJF, Kwok JT, Yang Q, Chen YQ (2006) Multidimensional vector regression for accurate and low-cost location estimation in pervasive computing. IEEE Trans Knowl Data Eng 18(9):1181–1193

    Article  Google Scholar 

  19. Gavalas D, Mpitziopoulos A, Pantziou G, Konstantopoulos C (2010) An approach for near-optimal distributed data fusion in wireless sensor networks. Wirel Netw 16(5):1407–1425

    Article  Google Scholar 

  20. Hugh DW (2001) Multi Sensor Data Fusion. University of Sydney

  21. Chen GZ, Luo CM, Zhang L (2011) Duality cooperative sensing strategy of moving target for chain-type wireless sensor networks. Chin J Sci Instrum 32(6):1225–1231

    Google Scholar 

  22. Deligiannis N, Louvros S (2010) Hybrid TOA-AOA location positioning techniques in GSM networks. Wirel Pers Commun 54(2):321–348

    Article  Google Scholar 

  23. Hwang SJ, Grauman K (2012) Learning the relative importance of objects from tagged images for retrieval and cross-modal search. Int J Comput Vis 100(2):134–153

    Article  MathSciNet  Google Scholar 

  24. Hardoon D, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16:2639–2664

    Article  MATH  Google Scholar 

  25. Chen D, Liu ZX, Wang LZ, Dou MG, Chen JY, Li H (2013) Natural disaster monitoring with wireless sensor networks: a case study of data-intensive applications upon low-cost scalable systems. Mob Netw Appl 18(5):651–663

    Article  Google Scholar 

  26. Luo RC, Chen O (2009) Indoor human dynamic localization and tracking based on sensory data fusion techniques. In Proceeding of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, Louis, USA, Oct, pp: 860–865

  27. Yu N, Wan JW, Feng RJ (2008) Localization refinement algorithms for wireless sensor networks. Chin High Technol Lett 18(10):1017–1022

    Google Scholar 

  28. Boukhatem L, Friedmann L (2009) Multi-sink relocation with constrained movement in wireless sensor networks. Ad Hoc Sensor Wirel Netw 8(3):211–233

    Google Scholar 

Download references

Acknowledgments

This work was done with support of China University of Mining and Technology. In addition, this work was supported by the National High Technology Research and Development Program of China (2013AA06A411), the Graduate Education Innovation Project of Jiangsu Province (CXZZ12_0925), and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. The authors would like to thank the anonymous reviewers for their helpful comments which have improved the quality of the paper.

Author information

Authors and Affiliations

  1. School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Chengming Luo, Wei Li, Hai Yang, Mengbao Fan & Xuefeng Yang

Authors
  1. Chengming Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hai Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mengbao Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuefeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, C., Li, W., Yang, H. et al. Mobile Target Positioning Using Refining Distance Measurements with Inaccurate Anchor Nodes in Chain-Type Wireless Sensor Networks. Mobile Netw Appl 19, 363–381 (2014). https://doi.org/10.1007/s11036-014-0511-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-014-0511-1

Keywords

Search

Navigation