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2020-12-15

AE-STAP Algorithm for Space-Time Anti-Jamming

By Ruiyan Du, Fulai Liu, Xiaodan Chen, and Jiaqi Yang
Progress In Electromagnetics Research M, Vol. 99, 191-200, 2021
doi:10.2528/PIERM20091401

Abstract

Space-time adaptive processing (STAP) algorithms can provide effective interference suppression potential in global navigation satellite system (GNSS). However, the performance of these algorithms is limited by the training samples support in practical applications. This paper presents an effective STAP based on atoms extension (named as AE-STAP) algorithm to provide better anti-jamming performance even if within a very small number of snapshots. In the proposed algorithm, a spatial-temporal plane is constructed firstly by the sparsity of received signals in the spatial domain. In the plane, each grid point corresponds to a space-time steering vector, named as an atom. Then, the optimal atoms are selected by searching atoms that best match with the received signals in the spatial-temporal plane. These space-time steering vectors corresponding to the optimal atoms are used to construct the interference subspace iteratively. Finally, in order to improve the estimation accuracy of interference subspace, an atoms extension (AE) method is given by extending the optimal atoms in a diagonal manner. The STAP weight vector is obtained by projecting the snapshots on the subspace orthogonal to the interference subspace. Simulation results demonstrate that the proposed method can provide better interference suppression performance and higher output signal-to-interference-plus-noise ratios (SINRs) than the previous works.

Citation


Ruiyan Du, Fulai Liu, Xiaodan Chen, and Jiaqi Yang, "AE-STAP Algorithm for Space-Time Anti-Jamming," Progress In Electromagnetics Research M, Vol. 99, 191-200, 2021.
doi:10.2528/PIERM20091401
http://www.jpier.org/PIERM/pier.php?paper=20091401

References


    1. Zhu, Z. S. and C. Li, "Study on real-time identification of GNSS multipath errors and its application," Aerospace Science and Technology, Vol. 52, 215-223, 2016.
    doi:10.1016/j.ast.2016.02.032

    2. Xie, F., et al., "A jamming tolerant BeiDou combined B1/B2 vector tracking algorithm for ultra-tightly coupled GNSS/INS systems," Aerospace Science and Technology, Vol. 70, 265-276, 2017.
    doi:10.1016/j.ast.2017.08.019

    3. Melvin, W. L., "Space-time adaptive radar performance in heterogeneous clutter," IEEE Trans. Aerosp. Electron. Syst., Vol. 36, No. 2, 621-633, 2000.
    doi:10.1109/7.845251

    4. Wang, Y., Y. Peng, and Z. Bao, "Space-time adaptive processing for airborne radar with various array orientation," IEE Radar, Son. Navig, Vol. 144, No. 6, 330-340, 1997.
    doi:10.1049/ip-rsn:19971606

    5. Wang, H. and L. Cai, "On adaptive spatial-temporal processing for airborne surveillance radar systems," IEEE Trans. Aerosp. Electron. Syst., Vol. 30, No. 3, 660-670, 1994.
    doi:10.1109/7.303737

    6. Wang, Y. L., et al., "Robust space-time adaptive processing for airborne radar in nonhomogeneous clutter environments," IEEE Trans. Aerosp. Electron. Syst., Vol. 39, No. 1, 70-81, 2003.
    doi:10.1109/TAES.2003.1188894

    7. Wang, X. Y., Z. C. Yang, and R. C. de Lamare, "Robust two-stage reduced-dimension sparsity-aware STAP for airborne radar with coprime arrays," IEEE Transactions On Signal Processing, Vol. 68, 81-96, 2020.
    doi:10.1109/TSP.2019.2957640

    8. Haimovich, A. M., "An eigencanceler: Adaptive radar by eigenanalysis methods," IEEE Trans. Aerosp. Electron. Syst., Vol. 32, No. 2, 532-542, 1996.
    doi:10.1109/7.489498

    9. Goldstein, J. S. and I. S. Reed, "Reduced rank adaptive filtering," IEEE Trans. Signal Process, Vol. 45, No. 2, 492-496, 1997.
    doi:10.1109/78.554317

    10. Myrick, W. L., M. D. Zoltowski, and J. S. Goldstein, "Low-sample performance of reduced-rank power minimization based jammer suppression for GPS," IEEE Sixth International Symposium on Spread Spectrum Techniques and Applications, Vol. 1, 91-97, 2000.

    11. Jeon, H., et al., "Clutter covariance matrix estimation using weight vectors in knowledge-aided STAP," Electronics Letters, Vol. 53, No. 8, 560-562, 2017.
    doi:10.1049/el.2016.4631

    12. Wang, Z., et al., "Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar," Signal Process, Vol. 130, 159-168, 2017.
    doi:10.1016/j.sigpro.2016.06.023

    13. Han, S., C. Fan, and X. Huang, "A novel STAP based on spectrum-aided reduced-dimension clutter sparse recovery," IEEE Geosci. Remote Sens. Lett., Vol. 14, No. 2, 213-217, 2017.
    doi:10.1109/LGRS.2016.2635104

    14. Duan, K. Q., et al., "Off-grid effects mitigation exploiting knowledge of the clutter ridge for sparse recovery STAP," IET Radar, Sonar & Navigation, Vol. 12, 557-564, 2018.
    doi:10.1049/iet-rsn.2017.0425

    15. Dai, J., et al., "Root sparse bayesian learning for off-grid DOA estimation," IEEE Signal Process. Lett., Vol. 24, No. 1, 46-50, 2017.
    doi:10.1109/LSP.2016.2636319

    16. Wen, C., X. Xie, and G. Shi, "Off-grid DOA estimation under nonuniform noise via variational Sparse Bayesian learning," Signal Processing, Vol. 137, 69-79, 2017.
    doi:10.1016/j.sigpro.2017.01.020

    17. Bai, G., et al., "Parameter-searched OMP method for eliminating basis mismatch in space-time spectrum estimation," Signal Processing, Vol. 138, 11-15, 2017.
    doi:10.1016/j.sigpro.2017.03.003

    18. Li, Z., et al., "Off-grid STAP algorithm based on local search orthogonal matching pursuit," 2019 IEEE 4th International Conference on Signal and Image Processing, 187-191, 2019.
    doi:10.1109/SIPROCESS.2019.8868509

    19. Capon, J., "High-resolution frequency-wavenumber spectrum analysis," Proceedings of the IEEE, Vol. 57, No. 8, 1408-1418, 1969.
    doi:10.1109/PROC.1969.7278

    20. Liu, F. L., et al., "CS-SFD algorithm for GNSS anti-jamming receivers," Progress In Electromagnetics Research M, Vol. 79, 91-100, 2019.
    doi:10.2528/PIERM18121001

    21. Compton, R. T. and J. R. Russer, "The power-inversion adaptive array: concept and performance," IEEE Transactions on Aerospace and Electronic Systems, Vol. 15, No. 6, 803-815, 1979.
    doi:10.1109/TAES.1979.308765

    22. Wang, W., et al., "Interference suppression with flat gain constraint for satellite navigation systems," Radar Sonar & Navigation Iet, Vol. 9, No. 7, 852-856, 2015.
    doi:10.1049/iet-rsn.2014.0258