Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Off-Grid DOA Estimation Based on Alternating Iterative Weighted Least Squares for Acoustic Vector Hydrophone Array

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In this paper, an alternating iterative weighted least squares method is proposed to handle the off-grid issue in sparsity-based direction of arrival (DOA) estimation for acoustic vector hydrophone (AVH) array. Firstly, the off-grid model via AVH array is formulated by introducing a bias parameter into the signal model. Secondly, the reconstructed interference plus noise covariance matrix is calculated as the weighting term. Then, a novel objective function with respect to the sparse signal and the unknown bias parameter is developed based on weighted least squares. Finally, the closed-form solutions of the sparse signal and the unknown bias parameter are deduced. Simulation results reveal that compared with the state-of-the-art algorithms, the proposed method improves the DOA estimation accuracy in the presence of a coarse sample grid and has a faster convergence speed. Furthermore, the effectiveness and robustness of the proposed method are verified by the underwater experimental results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. A. Agarwal, A. Kumar, M. Agrawal, “Iterative adaptive approach to DOA estimation with acoustic vector sensors,” in Oceans, pp. 1–8 (2015)

  2. M.K. Awad, K.T. Wong, Recursive least-squares source tracking using one acoustic vector sensor. IEEE Trans. Aerosp. Electron. Syst. 48(4), 3073–3083 (2012)

    Article  Google Scholar 

  3. A. Bereketli, M.B. Guldogan, T. Kolcak, T. Gudu, A.L. Avsar, Experimental results for direction of arrival estimation with a single acoustic vector sensor in shallow water. J. Sens. (2015). https://doi.org/10.1155/2015/401353

    Article  Google Scholar 

  4. E.J. Candes, The restricted isometry property and its implications for compressed sensing. C.R. Math. 346(9–10), 589–592 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. F. Chen, J. Dai, N. Hu, Z. Ye, Sparse bayesian learning for off-grid doa estimation with nested arrays. Digital Signal Process. 82, 187–193 (2018)

    Article  Google Scholar 

  6. J. Dai, X. Bao, W. Xu, C. Chang, Root sparse bayesian learning for off-grid doa estimation. IEEE Signal Process. Lett. 24(1), 46–50 (2016)

    Article  Google Scholar 

  7. P. Felisberto, P. Santos, S.M. Jesus, Acoustic pressure and particle velocity for spatial filtering of bottom arrivals. IEEE J. Oceanic Eng. 44(1), 179–192 (2019)

    Article  Google Scholar 

  8. A. Gretsistas, M.D. Plumbley, “An alternating descent algorithmfor the off-grid doa estimation problem with sparsity constraints,” in 2012 Proceedings of the 20th European Signal ProcessingConference(EUSIPCO). IEEE, 2012, pp. 874–878

  9. M. Hawkes, A. Nehorai, Acoustic vector-sensor correlations in ambient noise. IEEE J. Oceanic Eng. 26(3), 337–347 (2001)

    Article  Google Scholar 

  10. L. Hu, Z. Shi, J. Zhou, Q. Fu, Compressed sensing of complex sinusoids: an approach based on dictionary refinement. IEEE Trans. Signal Process. 60(7), 3809–3822 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. A. Jennings, J.J. McKeown, Matrix Computation (John Wiley & Sons Inc, New Jersey, 1992)

    MATH  Google Scholar 

  12. L. Jing, X. Wang, W. Zhou, Covariance vector sparsity-aware doa estimation for monostatic mimo radar with unknown mutual coupling. Sig. Process. 119, 21–27 (2012)

    Google Scholar 

  13. B. Kumar, A. Kumar, R. Bahl, Acoustic source localization accuracy using network of sonobuoys of omni-directional hydrophones and acoustic vector sensors. J. Acoust. Soc. India 45(1), 1–15 (2018)

    Google Scholar 

  14. J. Li, Z. Li, X. Zhang, Partial angular sparse representation based DOA estimation using sparse separate nested acoustic vector sensor array. Sensors 18(12), 1–15 (2018)

    Article  MathSciNet  Google Scholar 

  15. G. L. Liang, J. Fu, K. Zhang, G. P. Zhang, “Modified MVDR algorithm for DOA estimation using acoustic vector hydrophone,” in IEEE International Conference on Computer Science & Automation Engineering, pp. 327–330 (2011)

  16. L. Ma, T.A. Gulliver, A. Zhao, C. Ge, X. Bi, Underwater broadband source detection using an acoustic vector sensor with an adaptive passive matched filter. Appl. Acoust. 148(5), 162–174 (2019)

    Article  Google Scholar 

  17. D. Malioutov, M. Cetin, A.S. Willsky, A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans. Signal Process. 53(8), 3010–3022 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  18. S. Najeem, K. Kiran, A. Malarkodi, G. Latha, Open lake experiment for direction of arrival estimation using acoustic vector sensor array. Appl. Acoust. 119, 94–100 (2017)

    Article  Google Scholar 

  19. A. Nehorai, E. Paldi, Acoustic vector-sensor array processing. IEEE Trans. Signal Process. 42(9), 2481–2491 (1994)

    Article  Google Scholar 

  20. P. Santos, O. Rodriguez, P. Felisberto, S. Jesus, Seabed geoacoustic characterization with a vector sensor array. J. Acoust. Soc. Am. 128(5), 2652–2663 (2010)

    Article  Google Scholar 

  21. S.-G. Shi, Y. Li, Z.-R. Zhu, J. Shi, Real-valued robust doa estimation method for uniform circular acoustic vector sensor arrays based on worst-case performance optimization. Appl. Acoust. 148, 495–502 (2019)

    Article  Google Scholar 

  22. W. Tan, X. Feng, W. Tan, G. Liu, X. Ye, C. Li, “An iterative adaptive dictionary learning approach for multiple snapshot DOA estimation,” in 2018 14th IEEE International Conference on Signal Processing (ICSP). IEEE, pp. 214–219 (2018)

  23. W. Tan, X. Feng, Covariance matrix reconstruction for direction finding with nested arrays using iterative reweighted nuclear norm minimization. Int. J. Antennas Propag. 2019, 1–13 (2019)

    Article  Google Scholar 

  24. W. Tan, X. Feng, X. Ye, L. Jing, Direction-of-arrival of strictly non-circular sources based on weighted mixed-norm minimization. EURASIP J. Wirel. Commun. Netw. 2018(1), 1–10 (2018)

    Article  Google Scholar 

  25. P. Tichavsky, K.T. Wong, M.D. Zoltowski, Near-field/far-field azimuth and elevation angle estimation using a single vector hydrophone. IEEE Trans. Signal Process. 49(11), 2498–2510 (2001)

    Article  Google Scholar 

  26. C. Wang, J. Yin, D. Huang, A. Zielinski, Experimental demonstration of differential OFDM underwater acoustic communication with acoustic vector sensor. Appl. Acoust. 91, 1–5 (2015)

    Article  Google Scholar 

  27. X. Wang, L. Wang, X. Li, G. Bi, Nuclear norm minimization framework for DOA estimation in MIMO radar. Sig. Process. 135(6), 147–152 (2017)

    Article  Google Scholar 

  28. D. Wang, Y. Zou, W. Wang, Learning soft mask with DNN and DNN-SVM for multi-speaker DOA estimation using an acoustic vector sensor. J. Frankl. Inst. 355(4), 1692–1709 (2018)

    Article  MathSciNet  Google Scholar 

  29. C. Wen, X. Xie, G. Shi, Off-grid DOA estimation under nonuniform noise via variational sparse Bayesian learning. Sig. Process. 137, 69–79 (2017)

    Article  Google Scholar 

  30. X. Wu, W.-P. Zhu, J. Yan, “Gridless postprocessing for sparse signal reconstruction based DOA estimation,” in 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, pp. 684–688 (2015)

  31. X. Wu, W.-P. Zhu, J. Yan, Direction of arrival estimation for off-grid signals based on sparse Bayesian learning. IEEE Sens. J. 16(7), 2004–2016 (2016)

    Article  Google Scholar 

  32. K. Wu, V.G. Reju, A.W. Khong, Multisource DOA estimation in a reverberant environment using a single acoustic vector sensor. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 26(10), 1848–1859 (2018)

    Article  Google Scholar 

  33. X. Wu, W.-P. Zhu, J. Yan, Z. Zhang, Two sparse-based methods for off-grid direction-of-arrival estimation. Sig. Process. 142, 87–95 (2018)

    Article  Google Scholar 

  34. Z. Yang, L. Xie, C. Zhang, Off-grid direction of arrival estimation using sparse Bayesian inference. IEEE Trans. Signal Process. 61(1), 38–43 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  35. J. Yang, G. Liao, J. Li, An efficient off-grid DOA estimation approach for nested array signal processing by using sparse Bayesian learning strategies. Signal Process. 128, 110–122 (2016)

    Article  Google Scholar 

  36. M. Yang, D. Jin, B. Chen, Y. Xin, A multiscale sparse array of spatially-spread electromagnetic-vector-sensors for direction finding and polarization estimation. IEEE Access 6(99), 9807–9818 (2018)

    Article  Google Scholar 

  37. Y. Yang, Y. Zhang, L. Yang, Wideband sparse spatial spectrum estimation using matrix filter with nulling in a strong interference environment. J. Acoust. Soc. Am. 143(6), 3891–3898 (2018)

    Article  Google Scholar 

  38. Y. Zhang, Z. Ye, X. Xu, N. Hu, Off-grid DOA estimation using array covariance matrix and block-sparse Bayesian learning. Signal Process. 98, 197–201 (2014)

    Article  Google Scholar 

  39. L. Zhang, D. Wu, X. Han, Z. Zhu, Feature extraction of underwater target signal using mel frequency cepstrum coefficients based on acoustic vector sensor. J. Sens. (2016). https://doi.org/10.1155/2016/7864213

    Article  Google Scholar 

  40. Z. Zhang, J. He, T. Shu, W. Yu, Successive method for angle-polarization estimation with vector-sensor array. IEEE Sens. Lett. 2(1), 1–4 (2018)

    Article  Google Scholar 

  41. Z. Zhang, X. Wu, C. Li, W.P. Zhu, An \(\ell _p\)-norm based method for off-grid doa estimation. Circuits Syst. Signal Process. 38(2), 904–917 (2019)

    Google Scholar 

  42. A. Zhao, L. Ma, J. Hui, C. Zeng, X. Bi, Open-lake experimental investigation of azimuth angle estimation using a single acoustic vector sensor. J. Sens. (2018). https://doi.org/10.1155/2018/4324902

    Article  Google Scholar 

  43. C. Zhou, Y. Gu, X. Fan, Z. Shi, G. Mao, Y.D. Zhang, Direction-of-arrival estimation for coprime array via virtual array interpolation. IEEE Trans. Signal Process. 66(22), 5956–5971 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  44. C. Zhou, Y. Gu, Z. Shi, Y.D. Zhang, Off-grid direction-of-arrival estimation using coprime array interpolation. IEEE Signal Process. Lett. 25(11), 1710–1714 (2018)

    Article  Google Scholar 

  45. Y.X. Zou, B. Li, C.H. Ritz, Multi source doa estimation using an acoustic vector sensor array under a spatial sparse representation framework. Circuits Syst Signal Process. 35(3), 993–1020 (2016)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China under Grant 2016YFC1400203, National Natural Science Foundation of China under Grant 61531015, 61501374, 61771394 and Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2018JM6042.

Author information

Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Weidong Wang , Qunfei Zhang & Wentao Shi

  2. State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, People’s Republic of China

    Weijie Tan

  3. Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Linlin Mao

Authors
  1. Weidong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qunfei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wentao Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weijie Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Linlin Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wentao Shi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang , W., Zhang, Q., Shi, W. et al. Off-Grid DOA Estimation Based on Alternating Iterative Weighted Least Squares for Acoustic Vector Hydrophone Array. Circuits Syst Signal Process 39, 4650–4680 (2020). https://doi.org/10.1007/s00034-020-01391-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-020-01391-0

Keywords

Search

Navigation