Skip to main content
SpringerLink
Log in

2D-DOA Estimation Based on Higher-Order SVD-Based Using EMVS Sparse Array

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

Abstract

In this paper, we propose a tensor-based subspace algorithm for the two-dimensional direction of arrival (2D-DOA) estimation using a sparse array equipped with electromagnetic vector sensors (EMVS). Our approach capitalizes on the multidimensional characteristics of the collected data by arranging its covariance into a fourth-order tensor. Through the application of higher-order singular value decomposition, we improve signal subspace estimation compared to existing methods. To further enhance our algorithm, we integrate spatial rotation invariance techniques and vector cross-product methods. This combination enables automatic angle estimation without the need for pairing and without compromising aperture loss. Our proposed algorithm exhibits superior estimation performance, particularly in challenging scenarios characterized by low signal-to-noise ratios and limited snapshot availability. To validate the effectiveness and enhancements of our approach, we conduct numerical simulation experiments.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

My manuscript has no associated data.

References

  1. M.Y. Cao, X. Mao, X. Long, L. Huang, Tensor approach to DOA estimation of coherent signals with electromagnetic vector-sensor array. Sensors 18(12), 4320 (2018)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  2. Y. Chen, Z. Dong, K. Che, Z. Xiang, Cellular automaton model considering the effect of brake light and traffic light at the intersection. Int. J. Commun. Syst. 35(12), e4251 (2022)

    Article  Google Scholar 

  3. X. Fu, R. Cao, F. Wen, A de-noising 2-D-DOA estimation method for uniform rectangle array. IEEE Commun. Lett. 22(9), 1854–1857 (2018)

    Article  Google Scholar 

  4. M. Haardt, F. Roemer, G. Del Galdo, Higher-order SVD-based subspace estimation to improve the paramete estimation accuracy in multidimensional harmonic retrieval problems. IEEE Trans. Signal Process. 56(7), 3198–3213 (2008)

    Article  ADS  MathSciNet  Google Scholar 

  5. J. He, T. Shu, L. Li, T.K. Truong, Mixed near-field and far-field localization and array calibration with partly calibrated arrays. IEEE Trans. Signal Process. 70, 2105–2118 (2022)

    Article  ADS  MathSciNet  Google Scholar 

  6. H.A.L. Kiers, Towards a standardized notation and terminology in multiway analysis. J. Chemometr. J. Chemometr. Soc. 14(3), 105–122 (2000)

    Article  CAS  Google Scholar 

  7. T.G. Kolda, B.W. Bader, Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)

    Article  ADS  MathSciNet  Google Scholar 

  8. X. Lan, W. Liu, H.Y.T. Ngan, Joint DOA and polarization estimation with crossed-dipole and tripole sensor arrays. IEEE Trans. Aerosp. Electron. Syst. 56(6), 4965–4973 (2020)

    Article  ADS  Google Scholar 

  9. J. Li, P. Li, P. Li, L. Tang, X. Zhang, Q. Wu. Self-position awareness based on cascade direct localization over multiple source data. IEEE Trans. Intell. Transp. Syst. pp. 1–9, (2022)

  10. J. Li, Y. Li, H. Jiang, X. Zhang, Q. Wu, Multi-TDOA estimation and source direct position determination based on parallel factor analysis. IEEE Internet Things J. 10(8), 7405–7415 (2023)

    Article  Google Scholar 

  11. R.B.G. Paulo, L.F. de A. André, P. C. L. da C. João, D. G. Giovanni. Tensor-based methods for blind spatial signature estimation under arbitrary and unknown source covariance structure. Digit. Signal Process. 62:197–210, (2017)

  12. T. Shu, J. He, F. Wen, T.K. Truong, Incompletely polarized MIMO radar for target direction estimation. IEEE Trans. Radar Syst. 1, 532–541 (2023)

    Article  Google Scholar 

  13. C. Wang, L. Ai, F. Wen, J. Shi, An improved PARAFAC estimator for 2D-DOA estimation using EMVS array. Circuits Syst. Signal Process. 41(1), 147–165 (2022)

    Article  Google Scholar 

  14. X. Wang, Y. Guo, F. Wen, J. He, T. K. Truong. EMVS-MIMO radar with sparse Rx geometry: tensor modeling and 2D direction finding. IEEE Trans. Aerosp. Electron. Syst. 1–14, (2023)

  15. X. Wang, W. Wang, X. Li, J. Wang, A tensor-based subspace approach for bistatic MIMO radar in spatial colored noise. Sensors 14(3), 3897–3907 (2014)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  16. X. Wang, W. Wang, J. Liu, Q. Liu, B. Wang, Tensor-based real-valued subspace approach for angle estimation in bistatic MIMO radar with unknown mutual coupling. Signal Process. 116, 152–158 (2015)

    Article  Google Scholar 

  17. M. Wax, I. Ziskind, Detection of the number of coherent signals by the MDL principle. IEEE Trans. Acoust. Speech Signal Process. 37(8), 1190–1196 (1989)

    Article  Google Scholar 

  18. F. Wen, G. Gui, H. Gacanin, H. Sari, Compressive sampling framework for 2D-DOA and polarization estimation in mmWave polarized massive MIMO systems. IEEE Trans. Wirel. Commun. 22(5), 3071–3083 (2023)

    Article  Google Scholar 

  19. F. Wen, D. Ren, X. Zhang, G. Gui, B. Adebisi, H. Sari, F. Adachi. Fast localizing for anonymous UAVs oriented toward polarized massive MIMO systems. IEEE Internet Things J., (2023)

  20. F. Wen, J. Shi, G. Gui, H. Gacanin, O.A. Dobre, 3-D positioning method for anonymous UAV based on bistatic polarized MIMO radar. IEEE Internet Things J. 10(1), 815–827 (2023)

    Article  Google Scholar 

  21. K.T. Wong, X. Yuan, “vector cross-product direction-finding’’ with an electromagnetic vector-sensor of six orthogonally oriented but spatially noncollocating dipoles/loops. IEEE Trans. Signal Process. 59(1), 160–171 (2011)

    Article  ADS  MathSciNet  Google Scholar 

  22. K.T. Wong, M.D. Zoltowski, Closed-form direction finding and polarization estimation with arbitrarily spaced electromagnetic vector-sensors at unknown locations. IEEE Trans. Antenna. Propag. 48(5), 671–681 (2000)

    Article  ADS  Google Scholar 

  23. J. Wu, F. Wen, J. Shi, Direction finding in bistatic MIMO radar with direction-dependent mutual coupling. IEEE Commun. Lett. 25(7), 2231–2234 (2021)

    Article  Google Scholar 

  24. J. Wu, F. Wen, J. Shi, Fast angle estimation in MIMO system with direction-dependent mutual coupling. IEEE Commun. Lett. 25(9), 2913–2917 (2021)

    Article  Google Scholar 

  25. Y. Wu, G. Liao, H.C. So, A fast algorithm for 2-D direction-of-arrival estimation. Signal Process. 83(8), 1827–1831 (2003)

    Article  Google Scholar 

  26. N. Xi, L. Liping, 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 

  27. D. Zhang, Y. Zhang, G. Zheng, C. Feng, J. Tang, ESPRIT-like two-dimensional DOA estimation for monostatic MIMO radar with electromagnetic vector received sensors under the condition of gain and phase uncertainties and mutual coupling. Sensors 17(11), 2457 (2017)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  28. Z. Zhang, F. Wen, J. Shi, J. He, T.K. Truong, 2D-DOA estimation for coherent signals via a polarized uniform rectangular array. IEEE Signal Process. Lett. 30, 893–897 (2023)

    Article  ADS  Google Scholar 

  29. G. Zheng, C. Chen, Y. Song, Height measurement for meter wave MIMO radar based on matrix pencil under complex terrain. IEEE Trans. Veh. Technol. 72(9), 11844–11854 (2023)

    Article  Google Scholar 

Download references

Funding

This work was supported by National Natural Science Foundation of China (62271286).

Author information

Authors and Affiliations

  1. Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, China Three Gorges University, Yichang, 443002, China

    Mingzhou Gao, Zhe Zhang, Chaojun Yan & Fangqing Wen

  2. Institute of Vehicle Information Control and Network Technology, Hubei University of Automotive Technology, Shiyan, 442002, China

    Chaojun Yan & Fangqing Wen

Authors
  1. Mingzhou Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhe Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaojun Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fangqing Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chaojun Yan.

Ethics declarations

Conflict of interest

We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, M., Zhang, Z., Yan, C. et al. 2D-DOA Estimation Based on Higher-Order SVD-Based Using EMVS Sparse Array. Circuits Syst Signal Process 43, 1755–1772 (2024). https://doi.org/10.1007/s00034-023-02537-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-023-02537-6

Keywords

Search

Navigation