Gridless super-resolution sparse recovery for non-sidelooking STAP using reweighted atomic norm minimization

Abstract

The sparse recovery space–time adaptive processing (SR-STAP) can reduce the requirements of clutter samples and suppress clutter effectively using limited training samples for airborne radar. Commonly, the whole angle-Doppler plane is uniformly discretized into small grid points in SR-STAP methods. However, the clutter patches deviate from the pre-discretized grid points in a non-sidelooking SR-STAP radar. The off-grid effect degrades the SR-STAP performance significantly. In this paper, a gridless SR-STAP method based on reweighted atomic norm minimization is proposed, in which the clutter spectrum is precisely estimated in the continuous angle-Doppler domain without resolution limit. Numerical simulations are conducted and the results show that the proposed method can achieve better performance than the SR-STAP methods with discretized dictionaries and the SR-STAP methods utilizing atomic norm minimization.

This is a preview of subscription content, access via your institution.

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

References

  1. Adve, R. S., Hale, T. B., & Wicks, M. C. (2000a). Practical joint domain localised adaptive processing in homogeneous and nonhomogeneous environments. I. Homogeneous environments. IEE Proceedings—Radar, Sonar and Navigation, 147(2), 57–65. https://doi.org/10.1049/ip-rsn:20000035

    Article  Google Scholar 

  2. Adve, R. S., Hale, T. B., & Wicks, M. C. (2000b). Practical joint domain localised adaptive processing in homogeneous and nonhomogeneous environments. 2. Nonhomogeneous environments. IEE Proceedings—Radar Sonar and Navigation. https://doi.org/10.1049/ip-rsn:20000085

    Article  Google Scholar 

  3. Bai, G., Tao, R., Zhao, J., & Bai, X. (2017). Parameter-searched OMP method for eliminating basis mismatch in space-time spectrum estimation. Signal Processing, 138, 11–15.

    Article  Google Scholar 

  4. Bai, L., Roy, S., & Rangaswamy, M. (2013). Compressive radar clutter subspace estimation using dictionary learning. In Proceedings of IEEE Radar Conference, Ottawa, Ontario, Canada, 2013 (pp 1–6).

  5. Brown, R. D., Schneible, R. A., Wicks, M. C., Hong, W., & Yuhong, Z. (2000). STAP for clutter suppression with sum and difference beams. IEEE Transactions on Aerospace and Electronic Systems, 36(2), 634–646.

    Article  Google Scholar 

  6. Candès, E. J., & Fernandez-Granda, C. (2014). Towards a Mathematical Theory of Super-resolution. Communications on Pure and Applied Mathematics, 67(6), 906–956.

    MathSciNet  Article  Google Scholar 

  7. Chi, Y., & Chen, Y. (2015). Compressive two-dimensional harmonic retrieval via atomic norm minimization. IEEE Transactions on Signal Processing, 63(4), 1030–1042.

    MathSciNet  Article  Google Scholar 

  8. DiPietro, R. C. (1992). Extended factored space-time processing for airborne radar systems. In Proceedings of Asilomar Conference on Signals, Systems, and Computing, Pacific Grove, CA, USA.

  9. Duan, K., Liu, W., Duan, G., & Wang, Y. (2018). Off-grid effects mitigation exploiting knowledge of the clutter ridge for sparse recovery STAP. IET Radar Sonar & Navigation, 12(5), 557–564.

    Article  Google Scholar 

  10. Duan, K., Wang, Z., Xie, W., Chen, H., & Wang, Y. (2017). Sparsity-based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar. IET Signal Processing, 11(5), 544–553.

    Article  Google Scholar 

  11. Feng, W., Guo, Y., Zhang, Y., & Gong, J. (2018). Airborne radar space time adaptive processing based on atomic norm minimization. Signal Processing, 148, 31–40.

    Article  Google Scholar 

  12. Goldstein, J. S., Reed, I. S., & Scharf, L. L. (1998). A multistage representation of the Wiener filter based on orthogonal projections. IEEE Transactions on Information Theory, 44(7), 2943–2959.

    MathSciNet  Article  Google Scholar 

  13. Gu, Y., & Zhang, Y. D. (2018). Atomic Decomposition-based Sparse Recovery for Space-Time Adaptive Processing. In 2018 52nd Asilomar Conference on Signals, Systems, and Computers, (pp 1116–1120).

  14. Haimovich, A. (1996). The eigencanceler: Adaptive radar by eigenanalysis methods. IEEE Transactions on Aerospace and Electronic Systems, 32(2), 532–542.

    Article  Google Scholar 

  15. Klemm, R. (1987). Adaptive airborne MTI: An auxiliary channel approach. IEE Proceedings F—Communications, Radar and Signal Processing, 134(3), 269–276.

    Article  Google Scholar 

  16. Klemm, R. (2006). Principles of Space-Time Adaptive Processing: Institution of Engineering and Technology.

  17. Li, J., Zhu, X., Stoica, P., & Rangaswamy, M. (2010). High Resolution Angle-Doppler Imaging for MTI Radar. IEEE Transactions on Aerospace and Electronic Systems, 46(3), 1544–1556.

    Article  Google Scholar 

  18. Maria, S., & Fuchs, J. (2006). Application of the Global Matched Filter to Stap Data an Efficient Algorithmic Approach. In Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing, Toulouse, France, 2006 (pp. 1–4).

  19. Melvin, W. L., & Wicks, M. C. (1997). Improving practical space-time adaptive radar. In Proceedings of IEEE National Radar Conference, Syracuse, New York, USA,.

  20. Reed, I. S., Mallett, J. D., & Brennan, L. E. (1974). Rapid Convergence rate in adaptive arrays. IEEE Transactions on Aerospace and Electronic Systems, 10(6), 853–863.

    Article  Google Scholar 

  21. Sen, S. (2015). Low-Rank Matrix Decomposition and Spatio-Temporal Sparse Recovery for STAP Radar. IEEE Journal of Selected Topics in Signal Processing, 9(8), 1510–1523.

    Article  Google Scholar 

  22. Sun, K., Meng, H., Wang, Y., & Wang, X. (2011). Direct data domain STAP using sparse representation of clutter spectrum. Signal Processing, 91(9), 2222–2236.

    Article  Google Scholar 

  23. Tang, G., Bhaskar, B. N., Shah, P., & Recht, B. (2013). Compressed Sensing Off the Grid. IEEE Transactions on Information Theory, 59(11), 7465–7490.

    MathSciNet  Article  Google Scholar 

  24. Wang, Y. L., Cheng, J. W., Bao, Z., & Peng, Y. N. (2003). Robust space-time adaptive processing for airborne radar in nonhomogeneous clutter environments. IEEE Transactions on Aerospace and Electronic Systems, 39(1), 70–81.

    Article  Google Scholar 

  25. Ward, J. (1994). Space-time adaptive processing for airborne radar. Lexington, MA: MIT Lincoln Laboratory.

  26. Yang, K., Bar-Shalom, Y., Willett, P., Freund, Z., & Ben-Dov, R. (2017). Sparsity-Based STAP Using Alternating Direction Method with Gain/Phase Errors. IEEE Transactions on Aerospace & Electronic Systems, 53(6), 2756–2768.

    Article  Google Scholar 

  27. Yang, Z., Li, X., Wang, H., & Fa, R. (2016). Knowledge-aided STAP with sparse-recovery by exploiting spatio-temporal sparsity. IET Signal Processing, 10(2), 150–161.

    Article  Google Scholar 

  28. Yang, Z., & Xie, L. (2016). Enhancing sparsity and resolution via reweighted atomic norm minimization. IEEE Transactions on Signal Processing, 64(4), 995–1006.

    MathSciNet  Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China and Civil Aviation Administration of China (Grant No. U1733116), Fundamental Research Funds for Central Universities-CAUC(3122019048), Young Scholar Foundation of Civil Aviation University of China

Author information

Affiliations

Authors

Corresponding author

Correspondence to Tao Zhang.

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

Verify currency and authenticity via CrossMark

Cite this article

Zhang, T., Hu, Y. & Lai, R. Gridless super-resolution sparse recovery for non-sidelooking STAP using reweighted atomic norm minimization. Multidim Syst Sign Process (2021). https://doi.org/10.1007/s11045-021-00784-x

Download citation

Keywords

  • Airborne radar
  • Space–time adaptive processing
  • Off-grid
  • Reweighted atomic norm minimization