Skip to main content
Log in

A new DOA estimation algorithm based on compressed sensing

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Considering the computational complexity and redundancy of traditional array signal arrival angle (DOA) estimation algorithms, the compressed sensing technology was used to improve the real-time and accurate performance of the DOA estimation algorithm, in which, the space sparse signals were reconstructed from the array data by means of array manifold matrix. Compared with the classical MUSIC algorithm, the compressed sensing DOA estimation method could effectively improve the direction finding accuracy and angle resolution with low SNR and snapshot deficiency. Moreover, the proposed algorithm could achieve the coherent signal estimation correctly, and the simulation results show that its performance was superior to that of traditional algorithm.

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

Access this article

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

Similar content being viewed by others

References

  1. Hu, N., Ye, Z., Xu, X., et al.: DOA estimation for sparse array via sparse signal reconstruction. IEEE Trans. Aerospace Electron. Syst. 49(2), 760–773 (2013)

    Article  Google Scholar 

  2. Xi, N., Liping, L.: A computationally efficient subspace algorithm for 2-D DOA estimation with L-shaped array. IEEE Signal Process. Lett. 21(8), 971–974 (2014)

    Article  Google Scholar 

  3. Yan, F.G., Jin, M., Qiao, X.: Low-complexity DOA estimation based on compressed MUSIC and Its performance analysis. IEEE Trans. Signal Process. 61(8), 1915–1930 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  4. Rahman, M.U.: Performance analysis of MUSIC DOA algorithm estimation in multipath environment for automotive radars. Int. J. Appl. Sci. Eng. 14(2), 125–132 (2016)

    Google Scholar 

  5. Ren, S., Ma, X., Yan, S., et al.: 2-D unitary ESPRIT-like direction of arrival estimation for coherent signals with a uniform rectangular array. Sensors 13(4), 4272–4288 (2013)

    Article  Google Scholar 

  6. Qian, C., Huang, L., So, H.C.: Computationally efficient ESPRIT algorithm for direction-of-arrival estimation based on Nyström method. Signal Process. 94, 74–80 (2014)

    Article  Google Scholar 

  7. Gu, J.F., Zhu, W.P., Swamy, M.N.S.: Joint 2-D DOA estimation via sparse L-shaped array. IEEE Trans. Signal Process. 63(5), 1171–1182 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  8. Qian, C., Huang, L., So, H.C.: Improved unitary root-MUSIC for DOA estimation based on pseudo-noise resampling. IEEE Signal Process. Lett. 21(2), 140–144 (2014)

    Article  Google Scholar 

  9. Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  10. Malloy, M.L., Nowak, R.D.: Near-optimal adaptive compressed sensing. IEEE Trans. Inf. Theory 60(7), 4001–4012 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Malioutov, D., Cetin, M., Willsky, A.: 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 

  12. Gribonval, R., Cevher, V., Davies, M.E.: Compressible distributions for high-dimensional statistics. IEEE Trans. Inf. Theory 58(8), 5016–5034 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  13. Rossi, M., Haimovich, A.M., Eldar, Y.C.: Spatial compressive sensing for MIMO radar. IEEE Trans. Signal Process. 62(2), 419–430 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  14. Kim, L.O., Ye, J.: Compressive music: revisiting the link between compressive sensing and array signal processing. IEEE Trans. Inf. Theory 58(1), 278–301 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Liu, Z.M., Huang, Z.T., Zhou, Y.Y.: Sparsity-inducing direction finding for narrowband and wideband signals based on array covariance vectors. IEEE Trans. Wirel. Commun. 12(8), 3896–3907 (2013)

    Article  Google Scholar 

  16. Bo, L., Zeng-Hui, Z., Ju-Bo, Z.: Sparsity model and performance analysis of DOA estimation with compressive sensing. J. Electron. Inf. Technol. 36(3), 589–594 (2014)

    Google Scholar 

  17. Donoho, D.L., Huo, X.: Uncertainty principles and ideal atomic decomposition. IEEE Trans. Inf. Theory 47, 2845–2862 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  18. Gribonval, R., Nielsen, M.: Sparse representations in unions of bases. IEEE Trans. Inf. Theory 49, 3320–3325 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  19. Cai, T., Wang, L., Xu, G.W.: Stable recovery of sparse signals and an oracle inequality. IEEE Trans. Inf. Theory 56, 3516–3522 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  20. Dheringe, N.A., Bansode, B.N.: Performance evaluation and analysis of direction of arrival estimation using MUSIC, TLS ESPRIT and Pro ESPRIT algorithms. Perform. Eval. 4(6), 4948–4958 (2015)

    Google Scholar 

Download references

Acknowledgements

The authors wish to thank for the financial support of Natural Science Foundation of China (61573253, 61271321), Tianjin Natural Science Foundation (16JCYBJC16400), Tianjin Enterprise Science and Technology Project of Special Correspondent (17JCTPJC54700), Tianjin Science and Technology Project (16YFZCGX00360,16ZXZNGX00080), National Training Programs of Innovation and Entrepreneurship for Undergraduates (201610069007, 201710069023). The corresponding author is Professor Zhang Liyi.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhang Li-Yi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yong, Z., Li-Yi, Z., Jian-Feng, H. et al. A new DOA estimation algorithm based on compressed sensing. Cluster Comput 22 (Suppl 1), 895–903 (2019). https://doi.org/10.1007/s10586-018-1752-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1752-8

Keywords

Navigation