Table of Contents Author Guidelines Submit a Manuscript
International Journal of Antennas and Propagation
Volume 2017, Article ID 1260601, 7 pages
https://doi.org/10.1155/2017/1260601
Research Article

Localization of Near-Field Sources Based on Sparse Signal Reconstruction with Regularization Parameter Selection

1School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
2Key Laboratory of Wireless Sensor Networks and Communication, Shanghai Institute of Microsystem and Information Technology, Shanghai 200050, China

Correspondence should be addressed to Wei He; nc.ca.mis.liam@eh.iew

Received 9 November 2016; Accepted 9 April 2017; Published 11 May 2017

Academic Editor: María Elena de Cos Gómez

Copyright © 2017 Shuang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. C. Chen, R. E. Hudson, and K. Yao, “Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near-field,” IEEE Transactions on Signal Processing, vol. 50, no. 8, pp. 1843–1854, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. D. Huang and M. Barkat, “Near-field multiple source localization by passive sensor array,” IEEE Transactions on Antennas and Propagation, vol. 39, no. 7, pp. 968–975, 1991. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Challa and S. Shamsunder, “High-order subspace-based algorithms for passive localization of near-field sources,” in Proceedings of the 1995 Conference Record of the Twenty-Ninth Asilomar Conference on Signals, Systems and Computers, pp. 777–781, Pacific Grove, Calif, USA. View at Publisher · View at Google Scholar
  4. N. Yuen and B. Friedlander, “Performance analysis of higher order ESPRIT for localization of near-field sources,” IEEE Transactions on Signal Processing, vol. 46, no. 3, pp. 709–719, 1998. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Liang and D. Liu, “Passive localization of mixed near-field and far-field sources using two-stage {MUSIC} algorithm,” IEEE Transactions on Signal Processing, vol. 58, no. 1, pp. 108–120, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. J. Liang and D. Liu, “Passive localization of near-field sources using cumulant,” IEEE Sensors Journal, vol. 9, no. 8, pp. 953–960, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Li and D. Xie, “Compressed symmetric nested arrays and their application for direction-of-arrival estimation of near-field sources,” Sensors, vol. 16, article 1939, no. 11, 2016 pages.
  8. E. Grosicki, K. Abed-Meraim, and Y. Hua, “A weighted linear prediction method for near-field source localization,” IEEE Transactions on Signal Processing, vol. 53, no. 10, part 1, pp. 3651–3660, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. K. Abed-Meraim, Y. Hua, and A. Belouchrani, “Second-order near-field source localization: algorithm and performance analysis,” in Procceding of the Conference Record of the 30th Asilomar Conference on Signals, Systems and Computers, pp. 723–727, 1996.
  10. W. J. Zhi and M. Y. Chia, “Near-field source localization via symmetric subarrays,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '07), vol. 2, pp. II-1121–II-1124, Honolulu, Hawaii, USA, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Xie, H. Tao, X. Rao, and J. Su, “Comments on 'near-field source localization via symmetric subarrays',” IEEE Signal Processing Letters, vol. 22, no. 5, pp. 643-644, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Malioutov, M. Çetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Transactions on Signal Processing, vol. 53, no. 8, part 2, pp. 3010–3022, 2005. View at Publisher · View at Google Scholar · View at MathSciNet
  13. B. Wang, J. Liu, and X. Sun, “Mixed sources localization based on sparse signal reconstruction,” IEEE Signal Processing letters, vol. 19, no. 8, pp. 487–490, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Qin, Y. D. Zhang, Q. Wu, and M. G. Amin, “Structure-aware bayesian compressive sensing for near-field source localization based on sensor-angle distributions,” International Journal of Antennas and Propagation, vol. 2015, Article ID 783467, 15 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Tian and X. Sun, “Passive localization of mixed sources jointly using MUSIC and sparse signal reconstruction,” AEU—International Journal of Electronics and Communications, vol. 68, no. 6, pp. 534–539, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Hu, S. P. Chepuri, and G. Leus, “Near-field source localization using sparse recovery techniques,” in Proceedings of the International Conference on Signal Processing and Communications (SPCOM '14), pp. 1–5, Bangalore, India, July 2014. View at Publisher · View at Google Scholar
  17. K. Hu, S. P. Chepuri, and G. Leus, “Near-field source localization: sparse recovery techniques and grid matching,” in Proceedings of the IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM '14), pp. 369–372, A Coruña, Spain, June 2014. View at Publisher · View at Google Scholar
  18. J. Yin and T. Chen, “Direction-of-arrival estimation using a sparse representation of array covariance vectors,” IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4489–4493, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. B. Ottersten, P. Stoica, and R. Roy, “Covariance matching estimation techniques for array signal processing applications,” Digital Signal Processing, vol. 8, no. 3, pp. 185–210, 1998. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Grant, S. Boyd, and Y. Ye, “CVX: Matlab software for disciplined convex programming,” http://cvxr.com/cvx/.