Circuits, Systems, and Signal Processing

, Volume 39, Issue 1, pp 502–512 | Cite as

Passive Localization of Near-Field Sources Based on Overlapped Subarrays

  • Jie Sun
  • Caiyong Hao
  • Zhi ZhengEmail author
Short Paper


In this paper, a new algorithm for near-field source localization is proposed based on a uniform linear array (ULA). First, the direction-of-arrivals (DOAs) of near-field sources at two symmetric phase points are obtained by using two overlapped subarrays of the ULA and second-order statistics-based method. Next, the DOA of each source at the phase origin is determined by exploiting the geometric relationships between the source and two phase points. Finally, with the DOA estimates, the ranges of near-field sources can be estimated by one-dimensional search. The proposed method involves neither two-dimensional search nor higher-order cumulant calculations and avoids parameter pair-matching process. Moreover, it can achieve higher accuracy than the existing efficient method. Numerical simulations are presented to verify the performance of the proposed method.


Source localization Far-field Near-field Overlapped subarrays 



This work is supported in part by the National Natural Science Foundation of China under Grant 61701081, by the Sichuan Applied Basic Research Program under Grant 2019YJ0191, by the China Postdoctoral Science Foundation under Grant 2018M643449, by the Key Project of Sichuan Education Department of China under Grant 18ZA0221, by the Natural Science Foundation of Guangdong Province under Grant 2018A0303130064, and by the Fundamental Research Funds for the Central Universities of China under Grant 2672018ZYGX2018J003.


  1. 1.
    J.A. Chaaya, J. Picheral, S. Marcos, Localization of spatially distributed near-field sources with unknown angular spread shape. Signal Process. 106, 259–265 (2015)CrossRefGoogle Scholar
  2. 2.
    R. N. Challa, S. Shamsunder, High-order subspace-based algorithms for passive localization of near-field sources, in Proceedings 29th Asilomar Conference on Signals, Systems, and Computers, 2, Pacific Grove, CA. (1995) pp. 777–781Google Scholar
  3. 3.
    E. Grosicki, K. Abed-Meraim, Y. Hua, A weighted linear prediction method for near-field source localization. IEEE Trans. Signal Process. 53(10), 3651–3660 (2005)MathSciNetCrossRefGoogle Scholar
  4. 4.
    V.N. Hari, A.B. Premkumar, X. Zhong, A decoupled approach for near-field source localization using a single acoustic vector sensor. Circuits Syst. Signal Process. 32(2), 843–859 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Y.-D. Huang, M. Barkat, Near-field multiple source localization by passive sensor array. IEEE Trans. Antennas Propag. 39(7), 968–975 (1991)CrossRefGoogle Scholar
  6. 6.
    L. Jianzhong, Y. Wang, W. Gang, Signal reconstruction for near-field source localisation. IET Signal Process. 9(3), 201–205 (2015)CrossRefGoogle Scholar
  7. 7.
    H. Krim, M. Viberg, Two decades of array signal processing research: the parametric approach. IEEE Signal Process. Mag. 13(4), 67–94 (1996)CrossRefGoogle Scholar
  8. 8.
    J.-H. Lee, Y.-M. Chen, C.-C. Yeh, A covariance approximation method for near-field direction-finding using a uniform linear array. IEEE Trans. Signal Process. 43(5), 1293–1298 (1995)CrossRefGoogle Scholar
  9. 9.
    J. Liang, D. Liu, Passive localization of near-field sources using cumulant. IEEE Sensors J. 9(8), 953–960 (2009)CrossRefGoogle Scholar
  10. 10.
    J. Liang, X. Zeng, B. Ji, J. Zhang, F. Zhao, A computationally efficient algorithm for joint range-DOA-frequency estimation of near-field sources. Digital Signal Process. 19(4), 596–611 (2009)CrossRefGoogle Scholar
  11. 11.
    T. Qiu, P. Wang, A novel method for near-field source localization in impulsive noise environments. Circuits Syst. Signal Process. 35(11), 4030–4059 (2016)CrossRefGoogle Scholar
  12. 12.
    R. Roy, T. Kailath, ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Trans. Acoust., Speech, Signal Process 37(7), 984–995 (1989)CrossRefGoogle Scholar
  13. 13.
    R. Schmidt, Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)CrossRefGoogle Scholar
  14. 14.
    D. Starer, A. Nehorai, Passive localization of near-field sources by path following. IEEE Trans. Signal Process. 42(3), 677–680 (1994)CrossRefGoogle Scholar
  15. 15.
    A. Weiss, B. Friedlander, Range and bearing estimation using polynomial rooting. IEEE J. Ocean. Eng. 18(2), 130–137 (1993)CrossRefGoogle Scholar
  16. 16.
    Y. Wu, L. Ma, C. Hou, G. Zhang, J. Li, Subspace-based method for joint range and DOA estimation of multiple near-field sources. Signal Process. 86(8), 2129–2133 (2006)CrossRefGoogle Scholar
  17. 17.
    J. Xie, H. Tao, X. Rao, J. Su, Efficient method of passive localization for near-field noncircular sources. IEEE Antennas Wireless Propag. Lett. 14, 1223–1226 (2015)CrossRefGoogle Scholar
  18. 18.
    J. Xie, H. Tao, X. Rao, J. Su, Comments on near-field source localization via symmetric subarrays. IEEE Signal Process. Lett. 22(5), 643–644 (2015)CrossRefGoogle Scholar
  19. 19.
    J. Xie, H. Tao, X. Rao, J. Su, Localization of mixed far-field and near-field sources under unknown mutual coupling. Digital Signal Process. 50, 229–239 (2016)CrossRefGoogle Scholar
  20. 20.
    X. Yan, Y. Wen, G. Liu, Near-field wideband source localisation from the sparse recovery perspective via the spatial-only modelling of array data. IET Commun. 12(8), 907–913 (2018)CrossRefGoogle Scholar
  21. 21.
    W.-J. Zeng, X.-L. Li, H. Zou, X.-D. Zhang, Near-field multiple source localization using joint diagonalization. Signal Process. 89(2), 232–238 (2009)CrossRefGoogle Scholar
  22. 22.
    Z. Zheng, J. Sun, W.-Q. Wang, H. Yang, Classification and localization of mixed near-field and far-field sources using mixed-order statistics. Signal Process. 143, 134–139 (2018)CrossRefGoogle Scholar
  23. 23.
    Z. Zheng, M. Fu, W.-Q. Wang, H.C. So, Mixed far-field and near-field source localization based on subarray cross-cumulant. Signal Process. 150, 51–56 (2018)CrossRefGoogle Scholar
  24. 24.
    Z. Zheng, M. Fu, D. Jiang, W. Wang, S. Zhang, Localization of mixed far-field and near-field sources via cumulant matrix reconstruction. IEEE Sensors J. 18(18), 7671–7680 (2018)CrossRefGoogle Scholar
  25. 25.
    Z. Zheng, J. Lu, W.-Q. Wang, H. Yang, S. Zhang, An efficient method for angular parameter estimation of incoherently distributed sources via beamspace shift invariance. Digital Signal Process. 83, 261–270 (2018)CrossRefGoogle Scholar
  26. 26.
    Z. Zheng, W. Wang, H. Meng, H.C. So, H. Zhang, Efficient beamspace-based algorithm for two-dimensional DOA estimation of incoherently distributed sources in massive MIMO systems. IEEE Trans. Veh. Technol. 67(12), 11776–11789 (2018)CrossRefGoogle Scholar
  27. 27.
    Z. Zheng, W. Wang, Y. Kong, Y.D. Zhang, MISC array: a new sparse array design achieving increased degrees of freedom and reduced mutual coupling effect. IEEE Trans. Signal Process. 67(7), 1728–1741 (2019)MathSciNetCrossRefGoogle Scholar
  28. 28.
    W. Zhi, M.Y.-W. Chia, Near-field source localization via symmetric subarrays. IEEE Signal Process. Lett. 14(6), 409–412 (2007)CrossRefGoogle Scholar
  29. 29.
    W. Zuo, J. Xin, N. Zheng, A. Sano, Subspace-based localization of far-field and near-field signals without eigendecomposition. IEEE Trans. Signal Process. 66(17), 4461–4476 (2018)MathSciNetCrossRefGoogle Scholar
  30. 30.
    W. Zuo, J. Xin, W. Liu, N. Zheng, H. Ohmori, A. Sano, Localization of near-field sources based on linear prediction and oblique projection operator. IEEE Trans. Signal Process. 67(2), 415–430 (2019)MathSciNetCrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)ChengduChina
  2. 2.Institute of Electronic and Information Engineering of UESTC in GuangdongDongguanChina
  3. 3.Shenzhen Station of State Radio Monitoring CenterShenzhenChina

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