Advertisement

Robust Single-Step Locations of Strictly Noncircular Sources in the Impulsive Noise Environment

  • Jiexin Yin
  • Ding WangEmail author
Article
  • 5 Downloads

Abstract

Direct position determination (DPD) is a promising technique, which localizes transmitters directly from original sensor outputs without estimating intermediate parameters in a single step. Therefore, DPD improves the location accuracy and avoids the data association problem, compared with the conventional two-step methods. However, most of the existing DPDs are investigated for complex circular sources, neglecting the property of complex noncircular signals, and rely on the assumption that the noise is Gaussian distributed with finite second-order moments. This paper presents a robust single-step location algorithm for strictly noncircular sources intercepted by a moving array in the impulsive noise environment. First, an extended lower order infinity-norm covariance matrix is proposed by exploiting the noncircularity of signals. We prove that it is bounded and has the extended subspace structure without strict restrictions on the noise distribution, thus the extended noise subspaces are obtained at all positions of the moving array. Then in the light of the subspace data fusion idea, we extend it to the noncircular version and directly localize noncircular sources. Simulation results demonstrate that the proposed algorithm is effective in the Gaussian noise, and significantly outperforms other location algorithms in the impulsive noise.

Keywords

Direct position determination (DPD) Impulsive noise Noncircular signal Extended lower order infinity-norm covariance (ELOIC) matrix Subspace data fusion (SDF) 

Notes

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61201381, 61401513 and 61772548), China Postdoctoral Science Foundation (Grant No. 2016M592989), the Outstanding Youth Foundation of Information Engineering University (Grant No. 2016603201), and the Self-Topic Foundation of Information Engineering University (Grant No. 2016600701).

References

  1. 1.
    Yue, L., Ding, G., & Zheng, Z. (2015). Path planning in sensor localization with mobile anchors: Survey and challenges. In IEEE international conference on wireless communications and signal processing (pp. 1–6).Google Scholar
  2. 2.
    Ding, G., Wu, Q., Zou, Y., et al. (2012). Joint spectrum sensing and transmit power adaptation in interference-aware cognitive radio networks. Transactions on Emerging Telecommunications Technologies, 25(2), 231–238.CrossRefGoogle Scholar
  3. 3.
    Wax, M., & Kailath, T. (1985). Decentralized processing in sensor arrays. IEEE Transactions on Acoustics, Speech, and Signal Processing, 33(5), 1123–1129.CrossRefGoogle Scholar
  4. 4.
    Shen, J. Y., Molisch, A. F., & Salmi, J. (2011). Accurate passive location estimation using TOA measurements. IEEE Transactions on Wireless Communications, 11(6), 253–257.Google Scholar
  5. 5.
    Kan, C., Ding, G., Wu, Q., et al. (2018). Robust localization with crowd sensors: A data cleansing approach. Mobile Networks and Applications, 23(1), 108–118.CrossRefGoogle Scholar
  6. 6.
    Wax, M., & Kailath, T. (1983). Optimum localization of multiple sources by passive arrays. IEEE Transactions on Acoustics, Speech, and Signal Processing, 31(5), 1210–1217.CrossRefGoogle Scholar
  7. 7.
    Li, J., Yang, L., & Guo, F. (2016). Coherent summation of multiple short-time signals for direct positioning of a wideband source based on delay and Doppler. Digital Signal Processing, 48(C), 58–70.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Weiss, A. J., & Amar, A. (2005). Direct position determination of multiple radio signals. EURASIP Journal on Applied Signal Processing, 1, 37–49.zbMATHGoogle Scholar
  9. 9.
    Amar, A., & Weiss, A. J. (2007). A decoupled algorithm for geolocation of multiple emitters. Signal Processing, 87(10), 2348–2359.CrossRefGoogle Scholar
  10. 10.
    Tirer, T., & Weiss, A. J. (2016). High resolution direct position determination of radio frequency sources. IEEE Signal Processing Letters, 23(2), 192–196.CrossRefGoogle Scholar
  11. 11.
    Tzafri, L., & Weiss, A. J. (2016). High-resolution direct position determination using MVDR. IEEE Transactions on Wireless Communications, 15(9), 6449–6461.CrossRefGoogle Scholar
  12. 12.
    Demissie, B., Oispuu, M., & Ruthotto E. (2008). Localization of multiple sources with a moving array using subspace data fusion. In IEEE international conference on information fusion, pp. 1–7.Google Scholar
  13. 13.
    Abeida, H., & Delmas, J.-P. (2006). MUSIC-like estimation of direction of arrival for noncircular sources. IEEE Transactions on Signal Processing, 54(7), 2678–2690.CrossRefGoogle Scholar
  14. 14.
    Zhang, J. F., & Qiu, T. S. (2014). A novel covariation based noncircular sources direction finding method under impulsive noise environments. Signal Processing, 98(5), 252–262.CrossRefGoogle Scholar
  15. 15.
    Yin, J. X., Wu, Y., & Wang, D. (2016). An auto-calibration method for spatially and temporally correlated noncircular sources in unknown noise fields. Multidimensional Systems and Signal Processing, 27(2), 511–539.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Hari, K. V. S., & Lalitha, V. (2011). Subspace-based DOA estimation using fractional lower order statistics. In IEEE international conference on acoustics, speech, and signal processing, pp. 2580–2583.Google Scholar
  17. 17.
    Tang, Y., Xiong, X. Z., & Zhong, L. L. (2014). Time-delay estimation based on fractional lower order statistics. In IEEE international conference on wireless communications and sensor network, pp. 50–55.Google Scholar
  18. 18.
    Visuri, S., Oja, H., & Koivunen, V. (2001). Subspace-based direction-of-arrival estimation using nonparametric statistics. IEEE Transactions on Signal Processing, 49(9), 2060–2073.CrossRefGoogle Scholar
  19. 19.
    Belkacemi, H., & Marcos, S. (2007). Robust subspace-based algorithms for joint angle/Doppler estimation in non-Gaussian clutter. Signal Processing, 87(7), 1547–1558.CrossRefGoogle Scholar
  20. 20.
    Steinwandt, J., Roemer, F., & Haardt, M. (2016). Deterministic Cramér-Rao bound for strictly non-circular sources and analytical analysis of the achievable gains. IEEE Transactions on Signal Processing, 64(17), 4417–4431.MathSciNetCrossRefGoogle Scholar
  21. 21.
    Lalitha, V. (2004). Subspace-based DOA estimation under non-Gaussian and in impulsive noise using fractional lower order statistics. Master’s Thesis, Indian Institute of Science, Banglaore.Google Scholar
  22. 22.
    Zhang, X. D. (2004). Matrix analysis and application. Beijing: Tsinghua University Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Digital Switching System Engineering and Technology Research CenterZhengzhouChina
  2. 2.Zhengzhou Institute of Information Science and TechnologyZhengzhouChina

Personalised recommendations