Vol. 99
Latest Volume
All Volumes
PIERM 129 [2024] PIERM 128 [2024] PIERM 127 [2024] PIERM 126 [2024] PIERM 125 [2024] PIERM 124 [2024] PIERM 123 [2024] PIERM 122 [2023] PIERM 121 [2023] PIERM 120 [2023] PIERM 119 [2023] PIERM 118 [2023] PIERM 117 [2023] PIERM 116 [2023] PIERM 115 [2023] PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2020-12-15
AE-STAP Algorithm for Space-Time Anti-Jamming
By
Progress In Electromagnetics Research M, Vol. 99, 191-200, 2021
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
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., R. Sun, G. Kang, 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., J. W. Chen, Z. Bao, 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., Y. Chung, W. Chung, 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., W. Xie, K. Duan, 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., W. J. Liu, G. Q. Duan, 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., X. Bao, W. Xu, 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., R. Tao, J. Zhao, 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., Y. Zhang, Q. Gao, 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., L. Liu, 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., Q. Du, R. Wu, 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