Skip to main content
SpringerLink
Log in

Application of compressive sensing for fast solving multi-snapshot signal model in uniform linear arrays

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

This paper presents an efficient approach for rapidly solving the multiple snapshot signal model, aiming to achieve accurate estimation of the DOA while reconstructing the incident signals. The proposed method is based on compressed sensing (CS) and involves several steps. Firstly, a measurement matrix is introduced into the multi-snapshot model, resulting in a reduced number of snapshot received data. Secondly, the corresponding source signals are obtained using the orthogonal matching pursuit (OMP) algorithm, which serves as the measurement results of the original source signals. Thirdly, the original source signals are reconstructed using the discrete Fourier transform (DFT) basis or interpolated DFT basis as the sparse transform, combined with OMP. The algorithm’s principle is elaborated in detail, and numerical simulations demonstrate its accuracy and low complexity.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Shen, J., Yi, J., Wan, X., Xie, D., Cheng, F.: Robust DOA estimation for passive radar with target signals mixed in the reference channel. IEEE Geosci. Remote Sens. Lett. 18(3), 456–460 (2021)

    Article  ADS  Google Scholar 

  2. Xu, Z., Li, H., Yang, K.: A modified differential beamforming and its application for DOA estimation of low frequency underwater signal. IEEE Sensors J. 20(16), 8890–8902 (2020)

    Google Scholar 

  3. Vallet, P., Mestre, X., Loubaton, P.: Performance analysis of an improved MUSIC DOA estimator. IEEE Trans. Signal Process. 63(23), 6407–6422 (2015)

    Article  ADS  MathSciNet  Google Scholar 

  4. Zeng, X., Chen, G., Jiao, S., Fu, S., Dong, L.: High-accuracy and low-complexity DOA estimation algorithm for transmit-only diversity bistatic MIMO radar. IEEE Access 7, 118278–118286 (2019)

    Article  Google Scholar 

  5. Ha, U., Assana, S., Adib, F.: Contactless Seismocardiography via Deep Learning Radars. New York, NY, USA: Assoc. Comput. Mach., (2020).

  6. Chen, J., Zhang, D., Wu, Z., Zhou, F., Sun, Q., Chen, Y.: Contactless electrocardiogram monitoring with millimeter wave radar. IEEE Trans. Mob. Comput. (2022). https://doi.org/10.1109/TMC.2022.3214721

    Article  Google Scholar 

  7. Emadi, M., Miandji, E., Unger, J.: OMP-based DOA estimation performance analysis. Digit. Signal Process. 79, 57–65 (2018)

    Article  MathSciNet  Google Scholar 

  8. Aghababaiyan, K., Dehghani, M.: FOMP algorithm for direction of arrival estimation. Phys. Commun. 26, 170–174 (2017)

    Google Scholar 

  9. Han, L., Liu, X., Zhang, N., Wu, S., Zhu, J., Xu, Z.: Two-dimensional multi-snapshot newtonized orthogonal matching pursuit for Doa estimation. Digit. Signal Process. 121, 103313 (2022)

    Article  Google Scholar 

  10. Park, Y., Gerstoft, P., Lee, J.-H.: Difference-frequency MUSIC for DOAs. IEEE Signal Process. Lett. 29, 2612–2616 (2022)

    Article  ADS  Google Scholar 

  11. Yang, J., Yang, Y., Lu, J., Yang, L.: Iterative methods for DOA estimation of correlated sources in spatially colored noise fields. Signal Process. 185, 100–108 (2021)

    Article  Google Scholar 

  12. Li, W., Zhu, Z., Gao, W., Liao, W.: Stability and super-resolution of MUSIC and ESPRIT for multi-snapshot spectral estimation. IEEE Trans. Signal Process. 70, 4555–4570 (2022)

    Article  ADS  MathSciNet  Google Scholar 

  13. Bilen, C., Puy, G., Gribonval, R., Daudet, L.: Convex optimization approaches for blind sensor calibration using sparsity. IEEE Trans. Signal Process. 62(18), 4847–4856 (2014)

    Article  ADS  MathSciNet  Google Scholar 

  14. Camlica, S., Yetik, I.S., Arikan, O.: Sparsity based off-grid blind sensor calibration. Digit. Signal Process. 84, 80–92 (2018)

    Article  Google Scholar 

  15. Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)

    Article  MathSciNet  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62071004, Anhui Engineering Laboratory for Sports Health Information Monitoring Technology Development Project under Grant KF2022YB03, and the Natural Science Foundation of the Anhui Higher Education Institutions under Grant 2023AH051280.

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Anhui University, Hefei, 230601, Anhui, China

    Bingbing Chen & Yufa Sun

  2. School of Electronic and Information Engineering, Hefei Normal University, Hefei, 230601, Anhui, China

    Bingbing Chen

Authors
  1. Bingbing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yufa Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have made substantial contributions to the conception, design, and revision of the paper.

Corresponding author

Correspondence to Yufa Sun.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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

Chen, B., Sun, Y. Application of compressive sensing for fast solving multi-snapshot signal model in uniform linear arrays. SIViP 18, 1507–1513 (2024). https://doi.org/10.1007/s11760-023-02866-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02866-8

Keywords

Search

Navigation