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2018-08-27
A Novel Non-Homogeneous STAP Algorithm for Target-Like Signal Elimination Based on Sparse Reconstruction
By
Progress In Electromagnetics Research M, Vol. 72, 153-163, 2018
Abstract
Space-time adaptive processing (STAP) for airborne radar employs training samples to estimate clutter covariance matrix (CCM). However, the target-like signals contained in the training samples severely corrupt the accuracy of the CCM. This paper proposes a novel non-homogeneous STAP algorithm for target-like signal elimination based on reduced-dimension sparse reconstruction (RDSR) to overcome this issue. The proposed algorithm exploits the high-resolution angle-Doppler spectrum obtained by RDSR to estimate and eliminate target-like signals. Theoretical analysis and simulation results show that the proposed algorithm effectively suppresses clutter and improves the performance of STAP in non-homogeneous environments.
Citation
Qi Zhang Mingwei Shen Jianfeng Li Di Wu Dai-Yin Zhu , "A Novel Non-Homogeneous STAP Algorithm for Target-Like Signal Elimination Based on Sparse Reconstruction," Progress In Electromagnetics Research M, Vol. 72, 153-163, 2018.
doi:10.2528/PIERM18051006
http://www.jpier.org/PIERM/pier.php?paper=18051006
References

1. Brennan, L. E. and I. S. Reed, "Theory of adaptive radar," IEEE Transactions on Aerospace and Electronic Systems, Vol. 9, No. 2, 237-252, 1973.
doi:10.1109/TAES.1973.309792

2. Melvin, W. L., "A STAP overview," IEEE Aerospace and Electronic Systems Magazine, Vol. 19, No. 1, 19-35, 2004.
doi:10.1109/MAES.2004.1263229

3. Reed, I. S., J. D. Mallet, and L. E. Brennan, "Rapid convergence rate in adaptive arrays," IEEE Transactions on Aerospace and Electronic Systems, Vol. 10, No. 6, 853-863, 1974.
doi:10.1109/TAES.1974.307893

4. 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

5. Ciuonzo, D., A. D. Maio, and D. Orlando, "A unifying framework for adaptive radar detection in homogeneous plus structured interference — Part II: Detectors design," IEEE Transactions on Signal Processing, Vol. 64, No. 11, 2907-2919, 2016.
doi:10.1109/TSP.2016.2519005

6. Ciuonzo, D., A. D. Maio, and D. Orlando, "On the statistical invariance for adaptive radar detection in partially homogeneous disturbance plus structured interference," IEEE Transactions on Signal Processing, Vol. 65, No. 5, 1222-1234, 2017.
doi:10.1109/TSP.2016.2620115

7. Ciuonzo, D., D. Orlando, and L. Pallotta, "On the maximal invariant statistic for adaptive radar detection in partially homogeneous disturbance with persymmetric covariance," IEEE Signal Processing Letters, Vol. 23, No. 12, 1830-1834, 2016.
doi:10.1109/LSP.2016.2618619

8. Rangaswamy, M., P. Chen, J. H. Michels, and B. Himed, "A comparison of two non-homogeneity detection methods for space-time adaptive processing," Sensor Array and Multichannel Signal Processing Workshop Proceedings, 355-359, Rosslyn, VA, USA, Aug. 2002.

9. Tang, B., J. Tang, and Y. Peng, "Detection of heterogeneous samples based on loaded generalized inner product method," Digital Signal Process, Vol. 22, No. 4, 605-613, 2012.
doi:10.1016/j.dsp.2012.03.001

10. Shackelford, A. K., K. Gerlach, and S. D. Blunt, "Partially adaptive STAP using the FRACTA algorithm," IEEE Transactions on Aerospace and Electronic Systems, Vol. 45, No. 1, 58-69, 2009.
doi:10.1109/TAES.2009.4805263

11. Rabideau, D. J. and A. Steinhardt, "Improved adaptive clutter cancellation through data-adaptive training," IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 3, 879-891, 1999.
doi:10.1109/7.784058

12. Wu, Y. F., T. Wang, J. X. Wu, and J. Duan, "Training sample selection for space-time adaptive processing in heterogeneous environments," IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 4, 691-695, 2015.
doi:10.1109/LGRS.2014.2357804

13. Wu, Y. F., T. Wang, J. X. Wu, and J. Duan, "Robust training samples selection algorithm based on spectral similarity for space-time adaptive processing in heterogeneous interference environments," IET Radar, Sonar & Navigation, Vol. 9, No. 7, 778-782, 2015.
doi:10.1049/iet-rsn.2014.0285

14. Gao, Z. Q. and H. H. Tao, "Robust STAP algorithm based on knowledge-aided SR for airborne radar," IET Radar, Sonar & Navigation, Vol. 11, No. 2, 321-329, 2017.
doi:10.1049/iet-rsn.2016.0256

15. Sun, K., et al., "A novel STAP algorithm using sparse recovery technique," Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium, 336-339, Cape Town, South Africa, Jul. 2009.

16. Herman, M. A. and T. Strohmer, "High-resolution radar via compressed sensing," IEEE Transactions on Signal Processing, Vol. 57, No. 6, 2275-2284, 2009.
doi:10.1109/TSP.2009.2014277

17. Han, S. D., C. F. Fan, and X. T. Huang, "A novel STAP based on spectrum-aided reduceddimension clutter sparse recovery," IEEE Geoscience & Remote Sensing Letters, Vol. 14, No. 2, 213-217, 2017.
doi:10.1109/LGRS.2016.2635104

18. Duan, K. Q., et al., "Sparsity-based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar," IET Signal Processing, Vol. 12, No. 5, 544-553, 2017.
doi:10.1049/iet-spr.2016.0183

19. Yang, Z. C., X. Li, H. Q. Wang, and W. D. Jiang, "On clutter sparsity analysis in space–time adaptive processing airborne radar," IEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 5, 1214-1218, 2013.
doi:10.1109/LGRS.2012.2236639

20. Sen, S., "Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar," IEEE Journal of Selected Topics in Signal Processing, Vol. 9, No. 8, 1510-1523, 2015.
doi:10.1109/JSTSP.2015.2464187

21. Ji, C. X., et al., "An efficient adaptive clutter compensation algorithm for bistatic airborne radar based on improved OMP application," Progress In Electromagnetics Research M, Vol. 59, 203-212, 2017.
doi:10.2528/PIERM17060801

22. Klemm, R., "Applications of space-time adaptive processing," IET Digital Library, 2004.

23. Wang, H. and L. J. Cai, "On adaptive spatial-temporal processing for airborne surveillance radar systems," IEEE Transactions on Aerospace and Electronic Systems, Vol. 30, No. 3, 660-670, 1994.
doi:10.1109/7.303737

24. Klemm, R., "Principles of space-time adaptive processing,", The Institution of Engineering and Technology, London, UK, 2002.

25. Shen, M. W., et al., "An efficient data domain STAP algorithm based on reduced dimension sparse reconstruction," Acta Electronica Sinica, Vol. 42, No. 11, 2286-2290, 2014.

26. Shen, M. W., et al., "An efficient adaptive angle-doppler compensation approach for non-sidelooking airborne radar STAP," Sensors, Vol. 15, No. 6, 13121-13131, 2015.
doi:10.3390/s150613121