A Quadratic Programming Localization Based on TDOA Measurement

  • Guangzhe LiuEmail author
  • Jingyu Hua
  • Feng Li
  • Weidang Lu
  • Zhijiang Xu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


With the popularity of smart devices, applications based on location services have been widely used, and wireless positioning technology can provide accurate positioning information. However, due to the effect of non-line-of-sight (NLOS) errors, the performance of the system can drop significantly. Accordingly, this paper introduces the theory of quadratic programming optimization based on the research of the time difference of arrival (TDOA) theory and proposes an optimization algorithm that can effectively suppress the influence of NLOS error. Simulation results show that compared with other common wireless location algorithms, the proposed algorithm has more reliable positioning accuracy under different environment models and has better system stability.


Wireless localization The time difference of arrival (TDOA) Quadratic programming Non-line-of-sight propagation 



This paper was sponsored by the National Natural Science Foundation of China under grant No. 61471322.


  1. 1.
    Li H. Study of wireless sensor network applications in network optimization. Sens Transducers. 2013;157(10):180–9.Google Scholar
  2. 2.
    Vaghefi RM, Amuru SD, Buehrer RM. Improving mobile node tracking performance in NLOS environments using cooperation. In: IEEE international conference on communications; 2015. p. 6595–600.Google Scholar
  3. 3.
    Xu W, Quitin F, Leng M, et al. Distributed localization of a RF target in NLOS environments. IEEE J Sel Areas Commun. 2015;33(7):1317–30.CrossRefGoogle Scholar
  4. 4.
    Kireev A, Fokin G, Al-odhari AHA. TOA measurement processing analysis for positioning in NLOS conditions. In: Systems of signals generating and processing in the field of on board communications; 2018. p. 1–4.Google Scholar
  5. 5.
    Wang Y, Ho KC. An asymptotically efficient estimator in closed-form for 3-D AOA localization using a sensor network. IEEE Trans Signal Process. 2015;14(12):6524–35.Google Scholar
  6. 6.
    Kim R, Ha T, Lim H, et al. TDOA localization for wireless networks with imperfect clock synchronization. In: International conference on information networking; 2014. p. 417–21.Google Scholar
  7. 7.
    Gholami MR, Vaghefi RM, Ström EG. RSS-based sensor localization in the presence of unknown channel parameters. IEEE Trans Signal Process. 2013;61(15):3752–9.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Feng Y, Fritsche C, Gustafsson F, et al. TOA-based robust wireless geolocation and Cramér-Rao lower bound analysis in harsh LOS/NLOS environments. IEEE Trans Signal Process. 2013;61(9):2243–55.CrossRefGoogle Scholar
  9. 9.
    Long C, Wang Y, et al. A mobile localization strategy for wireless sensor network in NLOS conditions. China Commun. 2016;13(10):69–78.CrossRefGoogle Scholar
  10. 10.
    Fascista A, Ciccarese G, Coluccia A, Ricci G. A change-detection approach to mobile node localization in bounded domains. In: Conference on information sciences and system; 2015. p. 1–6.Google Scholar
  11. 11.
    Martin RK, Yan C, Fan HH, et al. Algorithms and bounds for distributed TDOA-based positioning using OFDM signals. IEEE Trans Signal Process. 2011;59(3):1255–68.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Qi Y, Kobayashi H, Suda H. Analysis of wireless geolocation in a non-line-of-sight environment. IEEE Trans Wirel Commun. 2006;5(3):672–81.CrossRefGoogle Scholar
  13. 13.
    Caffery JJ, Stuber GL. Subscriber location in CDMA cellular networks. IEEE Trans Veh Technol. 1998;47(2):406–16.CrossRefGoogle Scholar
  14. 14.
    Venkatraman S, Caffery JJ, You HR. A novel TOA location algorithm using LOS range estimation for NLOS environments. IEEE Trans Veh Technol. 2004;53(5):1515–24.CrossRefGoogle Scholar
  15. 15.
    Cheung KW, So HC, Ma WK, et al. A constrained least squares approach to mobile positioning: algorithms and optimality. EURASIP J Adv Signal Process. 2006.
  16. 16.
    Zheng X, Hua J, Zheng Z, et al. LLOP localization algorithm with optimal scaling in NLOS wireless propagations. In: Proceedings of IEEE international conference on electronics information and emergency communication; 2014. p. 45–8.Google Scholar
  17. 17.
    Chan YT, Ho KC. A simple and efficient estimator for hyperbolic location. IEEE Trans Signal Process. 2002;42(8):1905–15.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Guangzhe Liu
    • 1
    Email author
  • Jingyu Hua
    • 1
  • Feng Li
    • 1
  • Weidang Lu
    • 1
  • Zhijiang Xu
    • 1
  1. 1.College of Information EngineeringZhejiang University of TechnologyHangzhouChina

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