A No-Ambiguity Acquisition Algorithm Based on Correlation Shift for BOC (N, N)

  • Xiyan Sun
  • Fang Hao
  • Yuanfa JiEmail author
  • Suqing Yan
  • Qinwen Miao
  • Qiang Fu
Part of the Studies in Computational Intelligence book series (SCI, volume 810)


In the course of GPS modernization, Binary Offset Carrier (BOC) modulation technology is adopted to realize rational utilization of frequency, to avoid mutual interference between navigation signal frequency bands. Due to the multiple peaks of the auto-correlation function (ACF) of BOC modulated signal, an acquisition algorithm is proposed in this paper. This new method analyzed sub-correlation function of ACF, then, in the process of local design, it is designed one sub-signal and half-chip-shift sub-signal. It can achieve the homologous sub-correlation function of ACF by respectively correlating two local signals and received signal. The complexity of the algorithm as well as its detection probability based on the constant false alarm rate is analyzed. Simulations show that the proposed method can effectively solve the problem of ambiguous acquisition.


BOC Sub-correlation Unambiguity Correlation-shift Side-peak cancellation 



This work was supported by the National Natural Science Foundation of China (61561016, 11603041), Guangxi Information Science Experiment Center funded project, Department of Science and Technology of Guangxi Zhuang Autonomous Region (AC16380014, AA17202048, AA17202033).


  1. 1.
    Julien, O., Macaiubian, C., Cannon, M., et al.: ASPeCT: Unambiguous sine–BOC (n, n) acquisition/tracking technique for navigation applications. IEEE Trans. Aerosp. Electron. Syst. 43(1), 150–162 (2007)CrossRefGoogle Scholar
  2. 2.
    Julien O., et al.: A new unambiguous BOC (n, n) signal tracking technique. In: Proceedings of the European Navigation Conference GNSS (2004)Google Scholar
  3. 3.
    Yan, T., Wei, J.L., Tang, Z.P., et al.: Unambiguous acquisition/tracking technique for high order sine-phased binary offset carrier modulated signal. Wirel. Pers. Commun. 84(4), 2835–2857 (2015)CrossRefGoogle Scholar
  4. 4.
    Qi, J.M., Chen, J.P., Li, Z.B., et al.: Unambiguous BOC modulated signals synchronization technique. IEEE Commun. Lett. 16(7), 986–989 (2012)CrossRefGoogle Scholar
  5. 5.
    Ji, Y.F., Liu, Y., Zhen, W.M., et al.: An unambiguous acquisition algorithm based on unit correlation for BOC (n, n) signal. IEICE Trans. Commun. 100(8), 1507–1513 (2017)Google Scholar
  6. 6.
    Zhang, H.L., Ba, X.H., Chen, J., et al.: The unambiguous acquisition technology for BOC (m, n) signals. Aeronaut. Acta 38(4), 217–226 (2017)Google Scholar
  7. 7.
    Cao, X.L., Guo, C.J.: A new unambiguity acquisition algorithm for BOC (n, n) signal. Glob. Position. Syst. 41(6), 1–5 (2016)Google Scholar
  8. 8.
    Qian, B., Tian, M.H., Pan, C.S.: BOC modulated signal acquisition processor design based on FPGA. Command. Fire Control. 08(8), 129–132 (2011)Google Scholar
  9. 9.
    Burian, A., Lohan, E.S., Renfors, M.: Low-complexity unambiguous acquisition methods for BOC-modulated CDMA signals. Int. J. Satell. Commun. Netw. 26(6), 503–522 (2008)Google Scholar
  10. 10.
    Fishman, P., Betz, J.W.: Predicting performance of direct acquisition for the M-code signal. In: Proceedings of ION NMT, pp. 574–582 (2000)Google Scholar
  11. 11.
    Benedetto, F., Giunta, G., Lohan, E.S., et al.: A fast unambiguous acquisition algorithm for BOC-modulated signals. IEEE Trans. Veh. Technol. 62(3), 1350–1355 (2013)CrossRefGoogle Scholar
  12. 12.
    Underwater image dehazing using joint trilateral filter. Comput. Electr. Eng. 40(1), 41–50 (2014)Google Scholar
  13. 13.
    Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J. (2017).
  14. 14.
    Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl., 1–8 (2017)Google Scholar
  15. 15.
    Wound intensity correction and segmentation with convolutional neural networks. Concurr. Comput. Pract. Exp. (2017).
  16. 16.
    Low illumination underwater light field images reconstruction using deep convolutional neural networks. Futur. Gener. Comput. Syst. (2018).
  17. 17.
    Deep adversarial metric learning for cross-modal retrieval. World Wide Web J. (2018).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xiyan Sun
    • 1
  • Fang Hao
    • 1
  • Yuanfa Ji
    • 1
    Email author
  • Suqing Yan
    • 1
  • Qinwen Miao
    • 1
  • Qiang Fu
    • 1
  1. 1.Guangxi Key Laboratory of Precision Navigation Technology and ApplicationGuilin University of Electronic TechnologyGuilinChina

Personalised recommendations