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

DOA Estimation Using Sparse Representation of Beamspace and Element-Space Covariance Differencing

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

Abstract

In order to eliminate the effect of noise on the performance of the direction-of-arrival (DOA) estimation and reduce the computational complexity, a sparse representation (SR) DOA estimation method is proposed. The proposed method first utilizes the beamspace and element-space covariance differencing to eliminate noise. Afterward, it vectorizes the difference covariance matrix. In a sequence, it establishes a new SR model to complete DOA estimation. Compared to existing SR DOA estimation methods, the proposed method significantly reduces the computational complexity since the parameters to be solved in its SR cost function are regardless of the number of sources and the number of array elements. Simulation results show that in the case of the unknown number of sources and low signal-to-noise ratios (SNRs), the proposed method has high DOA resolution and estimation accuracy.

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

Similar content being viewed by others

Data Availability

Data will be available on request from the authors.

References

  1. A. Bental, A. Nemirovski, Lectures on modern convex optimization: analysis, algorithms, and engineering applications. Society for Industrial and Applied Mathematics. (2001)

  2. Y.F. Fang, H.Y. Wang, S.Q. Zhu, Reconstructing DOA estimation in the second-order statistic domain by exploiting matrix completion. Circ. Syst. Sig. Proc. 38, 1–19 (2019)

    Article  Google Scholar 

  3. A.B. Gershman, Direction finding using beamspace root estimator banks. IEEE Trans. Sig. Proc. 46, 3131–3135 (1998)

    Article  Google Scholar 

  4. Z.Q. He, Q.H. Liu, L.N. Jin, Low complexity method for DOA estimation using array covariance matrix sparse representation. Elect. Lett. 49(3), 228–229 (2013)

    Article  Google Scholar 

  5. Z.Q. He, Z.P. Shi, L. Huang, Covariance sparsity-aware DOA estimation for nonuniform noise. Dig. Sig. Proc. 28, 75–81 (2014)

    Article  Google Scholar 

  6. X. Jing, X. Liu, H. Liu, A sparse recovery method for DOA estimation based on the sample covariance vectors. Circ. Syst. Sig. Proc. 36(3), 1066–1084 (2017)

    Article  Google Scholar 

  7. G.L. Liang, K. Tao, Z. Fan, Adaptive beam space transformation generalized likelihood ratio test algorithm using acoustic vector sensor array. Acta Electonica Sinica. 43(1), 135–139 (2015)

    Google Scholar 

  8. A.F. Liu, D.S. Yang, S.G. Shi, Augmented subspace MUSIC method for DOA estimation using acoustic vector sensor array. IET Radar Sonar Nav. 13(6), 075–969 (2019)

    Google Scholar 

  9. D. Malioutov, M. Cetin, A sparse signal reconstruction perspective for source localization with sensor arrays. 53(8), 3010-3022 (2005)

  10. Y. Nesterov, A. Nemirovskii, Interior-point polynomial algorithms in convex programing. SIAM Studies in Applied Mathematics. 13(1994)

  11. A. Paulraj, T. Kailath, Eigenstructure methods for direction of arrival estimation in the presence of unknown noise fields. IEEE Trans. Acoust. Speech Signal Proc. 34(1), 13–20 (1986)

    Article  MathSciNet  Google Scholar 

  12. S. Prasad, R.T. Williams, A.K. Mahalanabis, A transform-based covariance differencing approach for some classes of parameter estimation problems. IEEE Trans. Acoust. Speech Signal Proc. 36, 631-641 (1988)

  13. R. Roy, T. Kailath, ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Trans. Acoust. Speech Signal Proc. 37(7), 984–995 (2002)

    Article  Google Scholar 

  14. R.O. Schmidt, Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)

    Article  Google Scholar 

  15. P. Stoica, P. Babu, J. Li, SPICE: a sparse covariance-based estimation method for array processing. IEEE Trans. Signal Process. 59(2), 629–638 (2011)

    Article  MathSciNet  Google Scholar 

  16. P. Stoica, P. Babu, SPICE and LIKES: Two hyperparameter-free methods for sparse-parameter estimation. Signal Proc. 92(7), 1580–1590 (2012)

    Article  Google Scholar 

  17. S.G. Shi, Y. Li, D.S. Yang, DOA estimation of coherent signals based on the sparse representation for acoustic vector-sensor arrays. Circ. Syst. Signal Proc. 39(7), 3553–3573 (2020)

    Article  Google Scholar 

  18. F. Tuteur, Y. Rockah, A new method for signal detection and estimation using the eigenstructure of the covariance difference. IEEE International Conference on Acoustics, Speech, and Signal Processing. (2811-2814)(1986)

  19. J. Yin, T. Chen, Direction-of-arrival estimation using a sparse representation of array covariance vectors. IEEE Trans. Signal Proc. 59(9), 4489–4493 (2011)

    Article  MathSciNet  Google Scholar 

  20. T. Ye, X.Y. Sun, Y.D. Qin, DOA estimation in unknown colored noise using covariance differencing and sparse signal recovery. J. China Univ. Posts Telecommun. 21(3), 106–112 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 61701133).

Author information

Authors and Affiliations

  1. Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin, 150001, China

    Aifei Liu, Shengguo Shi & Song Li

  2. Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin, 150001, China

    Aifei Liu, Shengguo Shi & Song Li

  3. College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, 150001, China

    Fujia Xu, Aifei Liu, Shengguo Shi, Song Li & Ying Li

Authors
  1. Fujia Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aifei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shengguo Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Song Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ying Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aifei Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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

Xu, F., Liu, A., Shi, S. et al. DOA Estimation Using Sparse Representation of Beamspace and Element-Space Covariance Differencing. Circuits Syst Signal Process 41, 1596–1608 (2022). https://doi.org/10.1007/s00034-021-01846-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-021-01846-y

Keywords

Search

Navigation