http://iet.metastore.ingenta.com
1887

Compressive sensing-based coprime array direction-of-arrival estimation

Compressive sensing-based coprime array direction-of-arrival estimation

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A coprime array has a larger array aperture as well as increased degrees-of-freedom (DOFs), compared with a uniform linear array with the same number of physical sensors. Therefore, in a practical wireless communication system, it is capable to provide desirable performance with a low-computational complexity. In this study, the authors focus on the problem of efficient direction-of-arrival (DOA) estimation, where a coprime array is incorporated with the idea of compressive sensing. Specifically, the authors first generate a random compressive sensing kernel to compress the received signals of coprime array to lower-dimensional measurements, which can be viewed as a sketch of the original received signals. The compressed measurements are subsequently utilised to perform high-resolution DOA estimation, where the large array aperture of the coprime array is maintained. Moreover, the authors also utilise the derived equivalent virtual array signal of the compressed measurements for DOA estimation, where the superiority of coprime array in achieving a higher number of DOFs can be retained. Theoretical analyses and simulation results verify the effectiveness of the proposed methods in terms of computational complexity, resolution, and the number of DOFs.

References

    1. 1)
      • 1. Van Trees, H.L.: ‘Detection, estimation, and modulation theory, Part IV: optimum array processing’ (Wiley, New York, 2002).
    2. 2)
      • 2. Ge, X., Tu, S., Mao, G., et al: ‘5G Ultra-dense cellular networks’, IEEE Wirel. Commun., 2016, 23, (1), pp. 7279.
    3. 3)
      • 3. Hu, B., Hua, C., Chen, C., et al: ‘MUBFP: multiuser beamforming and partitioning for sum capacity maximization in MIMO systems’, IEEE Trans. Veh. Technol., 2017, 66, (1), pp. 233245.
    4. 4)
      • 4. Tang, J., Wen, H., Hu, L., et al: ‘Associating MIMO beamforming with security codes to achieve unconditional communication security’, IET Commun., 2016, 10, (12), pp. 15221531.
    5. 5)
      • 5. Shi, Q., Liu, L., Xu, W., et al: ‘Joint transmit beamforming and receive power splitting for MISO SWIPT systems’, IEEE Trans. Wirel. Commun., 2014, 13, (6), pp. 32693280.
    6. 6)
      • 6. Moffet, A.T.: ‘Minimum-redundancy linear arrays’, IEEE Trans. Antennas Propag., 1968, 16, (2), pp. 172175.
    7. 7)
      • 7. Bloom, G.S., Golomb, S.W.: ‘Applications of numbered undirected graphs’, Proc. IEEE, 1977, 65, (4), pp. 562570.
    8. 8)
      • 8. Pal, P., Vaidyanathan, P.P.: ‘Nested arrays: A novel approach to array processing with enhanced degrees of freedom’, IEEE Trans. Signal Process., 2010, 58, (8), pp. 41674181.
    9. 9)
      • 9. Vaidyanathan, P.P., Pal, P.: ‘Sparse sensing with co-prime samplers and arrays’, IEEE Trans. Signal Process., 2011, 59, (2), pp. 573586.
    10. 10)
      • 10. Gu, Y., Zhou, C., Goodman, N.A., et al: ‘Coprime array adaptive beamforming based on compressive sensing virtual array signal’. Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Shanghai, China, 2016, pp. 29812985.
    11. 11)
      • 11. Zhou, C., Gu, Y., Song, W.-Z., et al: ‘Robust adaptive beamforming based on DOA support using decomposed coprime subarrays’. Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Shanghai, China, 2016, pp. 29862990.
    12. 12)
      • 12. Weng, Z., Djurić, P.M.: ‘A search-free DOA estimation algorithm for coprime arrays’, Digit. Signal Process., 2014, 24, pp. 2733.
    13. 13)
      • 13. Zhou, C., Shi, Z., Gu, Y., et al: ‘DECOM: DOA estimation with combined MUSIC for coprime array’. Proc. Int. Conf. Wireless Communication and Signal Processing (WCSP), Hangzhou, China, 2013, pp. 15.
    14. 14)
      • 14. Zhou, C., Shi, Z., Gu, Y., et al: ‘DOA estimation by covariance matrix sparse reconstruction of coprime array’. Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, 2015, pp. 23692373.
    15. 15)
      • 15. Pal, P., Vaidyanathan, P.P.: ‘Coprime sampling and the MUSIC algorithm’. Proc. IEEE Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop, Sedona, AZ, USA, 2011, pp. 289294.
    16. 16)
      • 16. Qin, S., Zhang, Y.D., Amin, M.G.: ‘Generalized coprime array configurations for direction-of-arrival estimation’, IEEE Trans. Signal Process., 2015, 63, (6), pp. 13771390.
    17. 17)
      • 17. Han, K., Nehorai, A.: ‘Wideband Gaussian source processing using a linear nested array’, IEEE Signal Process. Lett., 2013, 20, (11), pp. 11101113.
    18. 18)
      • 18. Shi, Z., Zhou, C., Gu, Y., et al: ‘Source estimation using coprime array: A sparse reconstruction perspective’, IEEE Sensors J., 2017, 17, (3), pp. 755765.
    19. 19)
      • 19. Zhang, Y.D., Amin, M.G., Himed, B.: ‘Sparsity-based DOA estimation using co-prime arrays’. Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Vancouver, Canada, 2013, pp. 39673971.
    20. 20)
      • 20. Zhou, C., Shi, Z., Gu, Y.: ‘Coprime array adaptive beamforming with enhanced degrees-of-freedom capability’. Proc. IEEE Radar Conf., Seattle, WA, USA, 2017, pp. 14.
    21. 21)
      • 21. Tan, Z., Eldar, Y.C., Nehorai, A.: ‘Direction of arrival estimation using co-prime arrays: A super resolution viewpoint’, IEEE Trans. Signal Process., 2014, 62, (21), pp. 55655576.
    22. 22)
      • 22. Pal, P., Vaidyanathan, P.P.: ‘A grid-less approach to underdetermined direction of arrival estimation via low rank matrix denoising’, IEEE Signal Process. Lett., 2014, 21, (6), pp. 737741.
    23. 23)
      • 23. Tan, Z., Nehorai, A.: ‘Sparse direction of arrival estimation using co-prime arrays with off-grid targets’, IEEE Signal Process. Lett., 2014, 21, (1), pp. 2629.
    24. 24)
      • 24. Donoho, D.L.: ‘Compressed sensing’, IEEE Trans. Inf. Theory, 2006, 52, (4), pp. 12891306.
    25. 25)
      • 25. Yu, W., Chen, C., He, T., et al: ‘Adaptive compressive engine for real-time electrocardiogram monitoring under unreliable wireless channels’, IET Commun., 2016, 10, (6), pp. 607615.
    26. 26)
      • 26. Gu, Y., Goodman, N.A., Hong, S., et al: ‘Robust adaptive beamforming based on interference covariance matrix sparse reconstruction’, Signal Process., 2014, 96, pp. 376381.
    27. 27)
      • 27. Wu, X., Zhu, W., Yan, J.: ‘Direction of arrival estimation for off-grid signals based on sparse bayesian learning’, IEEE Sensors J., 2016, 16, (7), pp. 20042016.
    28. 28)
      • 28. Ding, W., Yang, F., Liu, S., et al: ‘Structured compressive sensing-based non-orthogonal time-domain training channel state information acquisition for multiple input multiple output systems’, IET Commun., 2016, 10, (6), pp. 685690.
    29. 29)
      • 29. Gu, Y., Zhang, Y.D., Goodman, N.A.: ‘Optimized compressive sensing-based direction-of-arrival estimation in massive MIMO’. Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, LA, USA, 2017.
    30. 30)
      • 30. Gu, Y., Goodman, N.A., Ashok, A.: ‘Radar target profiling and recognition based on TSI-optimized compressive sensing kernel’, IEEE Trans. Signal Process., 2014, 62, (12), pp. 31943207.
    31. 31)
      • 31. Tibshirani, R.: ‘Regression shrinkage and selection via the LASSO’, J. Royal Stat. Soc. B, 1996, 58, (1), pp. 267288.
    32. 32)
      • 32. Chen, S.S., Donoho, D.L., Saunders, M.A.: ‘Atomic decomposition by basis pursuit’, SIAM J. Sci. Comput., 1998, 20, (1), pp. 3361.
    33. 33)
      • 33. Grant, M., Boyd, S.: ‘CVX: Matlab software for disciplined convex programming, version 2.1’. Available at http://cvxr.com/cvx, June 2015.
    34. 34)
      • 34. Wang, Y., Leus, G., Pandharipande, A.: ‘Direction estimation using compressive sampling array processing’. Proc. IEEE/SP Workshop Statist. Signal Process., Cardiff, UK, 2009, pp. 626629.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2016.1048
Loading

Related content

content/journals/10.1049/iet-com.2016.1048
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address