Skip to main content
Log in

Source localization using TDOA and FDOA measurements based on modified cuckoo search algorithm

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

This paper introduces a new algorithm for solving the localization problem of moving multiple disjoint sources using time difference of arrival and frequency difference of arrival. The localization of moving sources can be considered as a least-square problem. There are many algorithms used to solve this problem such as, two-step weighted least squares, constrained total least-square and practical constrained least-square. However, most of these algorithms suffer from either slow convergence or numerical instability and don’t attain Cramer–Rao lower bound. We introduce a free-gradient algorithm called cuckoo search which avoids the slow convergence problem. The cuckoo search provides a combined global and local search method. Simulation results show that the proposed algorithm achieves better performance than other algorithms and attains Cramer–Rao lower bound.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Han, K., Luo, J., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.

    Article  Google Scholar 

  2. Zhou, L., Naixue, X., Shu, L., Vasilakos, A., & Yeo, S.-S. (2010). Context-aware middleware for multimedia services in heterogeneous networks. IEEE Intelligent Systems, 25(2), 40–47.

    Article  Google Scholar 

  3. Xiao, Y., Peng, M., Gibson, J., Xie, G. G., Du, D.-Z., & Vasilakos, A. V. (2012). Tight performance bounds of multihop fair access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554.

    Article  Google Scholar 

  4. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3), 810–823.

    Article  Google Scholar 

  5. Xiong, N., Vasilakos, A. V., Yang, L. T., Song, L., Pan, Y., Kannan, R., & Li, Y. (2009). Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems. IEEE Journal on Selected Areas in Communications, 27(4), 495–509.

    Article  Google Scholar 

  6. Liu, X.-Y., Zhu, Y., Kong, L., Liu, C., Gu, Y., Vasilakos, A. V., & Wu, M.-Y. (2015). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2188–2197.

    Article  Google Scholar 

  7. Liu, Y., Xiong, N., Zhao, Y., Vasilakos, A. V., Gao, J., & Jia, Y. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.

    Article  Google Scholar 

  8. Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  Google Scholar 

  9. Rahimi, M. R., Liu, C. H., Vasilakos, A. V., & Venkatasubramanian, N. (2014). Mobile cloud computing: A survey, state of art and future directions. Mobile Networks and Applications, 19(2), 133–143.

    Article  Google Scholar 

  10. Acampora, G., Gaeta, M., Loia, V., & Vasilakos, A. V. (2010). Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Transactions on Autonomous and Adaptive Systems, 5(2), 1–26.

    Article  Google Scholar 

  11. Wei L., Zhu H., Cao Z., Jia W., & Vasilakos A.V. (2010). SecCloud: bridging secure storage and computation in cloud. In IEEE 30th international conference on distributed computing systems workshops (ICDCSW) (pp. 52–61; 21–25).

  12. Fadlullah, Z. M., Taleb, T., Vasilakos, A. V., Guizani, M., & Kato, N. (2010). DTRAB: Combating against attacks on encrypted protocols through traffic-feature analysis. IEEE/ACM Transactions on in Networking, 18(4), 1234–1247.

    Article  Google Scholar 

  13. Liu, L., Song, Y., Zhang, H., Ma, H., & Vasilakos, A. V. (2015). Physarum Optimization: A biology-inspired algorithm for the Steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 818–831.

    Article  MathSciNet  Google Scholar 

  14. Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273.

    Article  Google Scholar 

  15. Sabek, I., Youssef, M., & Vasilakos, A. V. (2015). ACE: An accurate and efficient multi-entity device-free WLAN localization system. IEEE Transactions on Mobile Computing, 14(2), 261–273.

    Article  Google Scholar 

  16. Bhuiyan, M. Z. A., Wang, G., & Vasilakos, A. V. (2015). Local area prediction based mobile target tracking in wireless sensor networks. IEEE Transactions on Computers, 64(7), 1968–1982.

    Article  MathSciNet  Google Scholar 

  17. Simakov, S. (2008). Localization in airborne multistatic sonars. IEEE Journal of Oceanic Engineering, 33(3), 278–288.

    Article  Google Scholar 

  18. Jiang, M., Niu, R., & Blum, R. S. (2011). Bayesian target location and velocity estimation for multiple-input multiple-output radar. IET Radar, Sensor and Navigation, 5(6), 666–670.

    Article  Google Scholar 

  19. Thanthry, N., Emmuadi, I., & Srikumar, A. (2009). SVSS: intelligent video surveillance system for aircraft. IEEE Aerospace and Electronic Systems Magazine, 24(10), 23–29.

    Article  Google Scholar 

  20. Frampton, K. D. (2006). Acoustic self-localization in a distributed sensor network. IEEE Sensors Journal, 6, 166–172.

    Article  Google Scholar 

  21. Yang, K., Wang, G., & Luo, Z. (2009). Efficient convex relaxation methods for robust target localization by a sensor network using time differences of arrivals. IEEE Transactions on Signal Processing, 57, 2775–2784.

    Article  MathSciNet  Google Scholar 

  22. Huang, Y., Benesty, J., Elko, G. W., & Mersereati, R. (2001). Real-time passive source localization: A practical linear-correction least squares approach. IEEE Transactions on Speech and Audio Processing, 9(8), 943–956.

    Article  Google Scholar 

  23. Chan, Y. T., & Ho, K. C. (1994). A simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing, 42(8), 1905–1915.

    Article  Google Scholar 

  24. Torrieri, D. J. (1984). Statistical theory of passive location systems. IEEE Transactions on Aerospace and Electronic Systems, 20(2), 183–198.

    Article  Google Scholar 

  25. Ho, K. C., & Xu, W. (2004). An accurate algebraic solution for moving source location using TDOA and FDOA measurements. IEEE Transactions on Signal Processing, 52(9), 2453–2463.

    Article  MathSciNet  Google Scholar 

  26. Ho, K. C., & Xu, W. (2007). Source localization using TDOA and FDOA measurements in the presence of receiver location errors: Analysis and solution. IEEE Transactions on Signal Processing, 55(2), 684–696.

    Article  MathSciNet  Google Scholar 

  27. Sun, M., & Ho, K. C. (2011). An asymptotically efficient estimator for TDOA and FDOA positioning of multiple disjoint sources in the presence of sensor location uncertainties. IEEE Transactions on Signal Processing, 59(7), 3434–3440.

    Article  MathSciNet  Google Scholar 

  28. Wei, H., Peng, R., Wan, Q., Chen, Z.-X., & Ye, S.-F. (2010). Multidimensional scaling analysis for passive moving target localization with TDOA and FDOA measurements. IEEE Transactions on Signal Processing, 58(3), 1677–1688.

    Article  MathSciNet  Google Scholar 

  29. Sun X. Y., Li, J. D., Huang P. Y., & Pang, J. Y. (2008). Total least squares solution of active target localization using TDOA and FDOA measurements in WSN. In Proceedings of IEEE 22nd international conference on advanced information networking and applications AINAW’08 (pp. 995–999), Okinawa.

  30. Zhang, H., & Zhang, Y. (2013). A robust algorithm for multiple disjoint moving sources localization with erroneous sensor locations. Journal of Communications, 8(6), 345–351.

    Article  Google Scholar 

  31. Huagang, Y., Huang, G., Gao, J., & Yan, B. (2012). Practical constrained least-square algorithm for moving source location using TDOA and FDOA measurements. Journal of Systems Engineering and Electronics, 23(4), 488–494.

    Article  Google Scholar 

  32. Cheung, K. W., So, H. C., Ma, W. K., et al. (2006). A constrained least squares approach to mobile positioning: algorithms and optimality. EURASIP Journal Applied Signal Processing, 2006, 1–23.

  33. Wang, G., & Chen, H. (2011). An importance sampling method for TDOA based source localization. IEEE Transactions on Wireless Communications, 10(5), 1560–1568.

    Article  Google Scholar 

  34. Goyal, S., & Patterh, M. S. (2014). Wireless sensor network localization based on cuckoo search algorithm. Wireless Personal Communications, 79(1), 223–234.

    Article  Google Scholar 

  35. Feng, L. (2013). The research on wireless sensor network node positioning based on cuckoo searching algorithm and DV-Hop Algorithm. Journal of Convergence Information Technology (JCIT), 8(6), 620–623.

    Article  Google Scholar 

  36. Sivakumar, S., & Venkatesan, R. (2014). Error minimization in localization of wireless sensor networks using modified cuckoo search with mobile anchor positioning (MCS-map) algorithm. International Journal of Computer Applications, 95(6), 1–8.

    Article  Google Scholar 

  37. Yang, X.-S, & Deb, S. (2009). Cuckoo search via Lévy flights. In Proceedings of world congress on nature and biologically inspired computing (NaBIC2009, India) (pp. 210–214), IEEE Publications, USA.

  38. Yang, X.-S., & Deb, S. (2010). Engineering optimization by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimization, 1, 330–430.

    Article  MATH  Google Scholar 

  39. Fateen, S. K., & Bonilla-Petriciolet, A. (2014). Gradient-based cuckoo search for global optimization. Hindawi Publishing Corporation Mathematical Problems in Engineering, 2014, 1–12.

    Article  Google Scholar 

  40. Walton, S., Hassan, O., Morgan, K., & Brown, M. R. (2011). Modified cuckoo search: A new gradient free optimisation algorithm. Chaos, Solitons and Fractals, 44(9), 710–718.

    Article  Google Scholar 

  41. Bratton, D., & Kennedy, J. (2007). Defining a standard for particle swarm optimization. In Swarm intelligence symposium, 2007. SIS 2007. IEEE (pp. 120–127).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Abd El Aziz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abd El Aziz, M. Source localization using TDOA and FDOA measurements based on modified cuckoo search algorithm. Wireless Netw 23, 487–495 (2017). https://doi.org/10.1007/s11276-015-1158-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-1158-y

Keywords

Navigation