Progress In Electromagnetics Research M
ISSN: 1937-8726
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By F. Liu, L. Liu, J. Yang, and M. Zhang

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Most of space-time adaptive processing methods have the excellent ability to suppress interferences when the space-time covariance matrix is perfectly estimated. Unfortunately, these methods may have calculation error of the covariance matrix in the case of fewer snapshots, which may lead to remarkable performance degrading. To solve the aforementioned problem, a space-frequency domain anti-jamming algorithm based on the compressed sensing theory (CS-SFD) is presented. Firstly, the proposed method utilizes less sampled data to form a space-frequency two-dimensional sparse representation for the narrowband interference signals. Secondly, the interference covariance matrix estimation problem is modeled as a sparse reconstruction problem which can be efficiently solved by the orthogonal matching pursuit algorithm. Furthermore, the diagonal loading method is used to modify the interference plus noise covariance matrix. Finally, the weight vector is given by the minimum output power criterion. Compared with the previous work, the presented method has better robustness and more effectively anti-jamming performance in the case of fewer snapshots. Simulation results show the effectiveness of the proposed algorithm.

F. Liu, L. Liu, J. Yang, and M. Zhang, "CS-SFD Algorithm for GNSS Anti-Jamming Receivers," Progress In Electromagnetics Research M, Vol. 79, 91-100, 2019.

1. Kaplan, D. E. and C. Hegarty, Understanding GPS: Principles and Application, Artech House Publishers, Massachusetts, USA, 2005.

2. Mukhopadhyay, M., B. K. Sarkar, and A. Chakraborty, "Augmentation of anti-jam GPS system using smart antenna with a simple DOA estimation algorithm," Progress In Electromagnetics Research, Vol. 67, 231-249, 2007.

3. Frost, III, L. O., "An algorithm for linearly constrained adaptive array processing," Proceedings of the IEEE, Vol. 60, No. 8, 926-935, 1972.

4. Widrow, B., P. E. Mantiey, L. J. Griffiths, and B. B. Goode, "Adaptive antenna systems," Proceedings of the IEEE, Vol. 55, No. 12, 2143-2159, 1967.

5. Compton, R. T., "The power-inversion adaptive array: Concept and performance," Aerospace & Electronic Systems, IEEE Transactions on AES, Vol. 15, No. 6, 803-814, 1979.

6. Fante, R. L. and J. J. Vacarro, "Cancellation of jammers and jammer multipath in a GPS receiver," IEEE Aerospace and Electronic Systems Magazine, Vol. 13, No. 11, 25-28, 1988.

7. Liu, F., R. Du, and X. Bai, "Virtual space-time adaptive beamforming method for space-time antijamming," Progress In Electromagnetics Research M, Vol. 58, 183-191, 2017.

8. Fante, R. L. and J. J. Vacarro, "Valuation of adaptive space-time-polarization cancellation of interference," 2002 IEEE Position Location and Navigation Symposium, 1-3, California, April, 2002.

9. Amin, M. G., X. Wang, Y. D. Zhang, F. Ahmad, and E. Aboutanios, "Sparse arrays and sampling for interference mitigation and DOA estimation in GNSS," Proceedings of the IEEE, 1-16, 2016.

10. Myrick, W. L., J. S. Goldstein, and M. D. Zoltowski, "Low complexity anti-jam space-time processing for GPS," IEEE International Conference on Acoustics IEEE, 2001.

11. Fernandez-Prades, C. and J. A. Fernandez-Rubio, "Robust space-time beamforming in GNSS by means of second-order cone programming," IEEE International Conference on Acoustics, 2004.

12. Li, W., B. Yang, and Y. Zhao, "Low-complexity non-uniform diagonal loading for robust adaptive beamforming," IEEE Applied Computational Electromagnetics Society Symposium, 2017.

13. Mu, P., D. Li, Q. Yin, and W. Guo, "Robust MVDR beamforming based on covariance matrix reconstruction," Science China Information Sciences, Vol. 56, No. 4, 1-12, 2013.

14. Hou, Y., L. Xue, and Y. Jin, "Robust adaptive beamforming method based on interference-plus-noise covariance matrix," IEEE International Conference on Signal Processing, 2013.

15. Liu, F., J. Wu, R. Du, and X. Bai, "Robust adaptive beamforming against the array pointing error," 2017 Progress In Electromagnetics Research Symposium - Fall (PIERS - FALL), 2782-2789, Singapore, November 19-22, 2017.

16. Qian, J., Z. He, J. Xie, and Y. Zhang, "Null broadening adaptive beamforming based on covariance matrix reconstruction and similarity constraint," Eurasip Journal on Advances in Signal, Vol. 1, 1-10, 2017.

17. Baraniuk, R. G., "Compressive sensing," IEEE Signal Processing Magazing, Vol. 24, No. 4, 118-124, 2007.

18. Tropp, J. and A. C. Gilbert, "Signal recorvery form random measurements via orthogonal matching pursuit," IEEE Trans. iNFORM, Vol. 53, No. 6, 4655-4666, 2007.

19. Ji, S., Y. Xue, and L. Carin, "Bayesian compressive sensing," IEEE Trans. Signal Process., Vol. 56, No. 6, 2346-2356, 2008.

20. Wu, Q., Y. D. Zhang, M. G. Amin, and B. Himed, "Space-time adaptive processing and motion parameter estimation in multistatic passive radar using sparse bayesian Learning," IEEE Transactions on Geoscience Remote Sensing, Vol. 54, No. 2, 944-957, 2016.

21. Duan, K., Z. Wang, W. Xie, H. Chen, and Y. Wang, "Sparsity-based STAP algorithm with multiple measurement vectors via sparse bayesian learning strategy for airborne radar," IET Signal Processing, Vol. 11, No. 5, 544-553, 2017.

22. Bai, G. T., R. Tao, J. Zhao, and X. Bai, "Parameter-searched OMP method for eliminating basis mismatch in space-time spectrum estimation," Signal Processing, Vol. 138, 11-15, 2017.

23. Sun, K., H. Zhang, G. Li, H. Meng, and X. Wang, "A novel STAP algorithm using sparse recovery technique," IEEE International Geoscience & Remote Sensing Symposium, 2009.

24. Wang, W. and R. Wu, "High resolution Direction of Arrival (DOA) estimation based on improved Orthogonal Matching Pursuit (OMP) algorithm by iterative local searching," Sensors, Vol. 13, No. 9, 11167-11183, 2013.

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