In the field of space-time adaptive processing (STAP), spare recovery type STAP (SR-STAP) algorithms exploit formulation of the clutter estimation problem in terms of sparse representation of a small number of clutter positions among a much larger number of potential positions in the angle-Doppler plane, and provide an effective approach to suppress the clutter especially in very short snapshots. However, it differs from many situations encountered by other SR application fields in the following ways: (i) it does not require to obtain the exact solution; (ii) it highly requires low-complexity approaches. In this paper, we focus on the performance analysis and parameters setting of STAP algorithms based on five representative fast SR techniques, namely, the compressive sampling matching pursuit, the sparse reconstruction by separable approximation, the fast iterative shrinkage-thresholding algorithm, the focal underdetermined system solution and the smoothed l0 norm method.
2. Guerci, J. R., Space-time Adaptive Processing for Radar, Artech House, 2003.
3. Melvin, W. L., "A STAP overview," IEEE Aerosp. Electron. Syst. Mag., Vol. 19, No. 1, 19-35, 2004.
4. Aïssa , B., M. Barkat, B. Atrouz, M. C. E. Yagoub, and M. A. Habib, "An adaptive reduced rank STAP selection with staggered PRF, effect of array dimensionality," Progress In Electromagnetics Research C, Vol. 6, 37-52, 2009.
5. Gong, Q. Y. and Z. D. Zhu, "Study STAP algorithm on interference target detect under nonhomogeneous environment," Progress In Electromagnetics Research, Vol. 99, 211-224, 2009.
6. Maria, S. and J. J. Fuchs, "Application of the global matched filter to STAP data an efficient algorithmic approach," Proc. IEEE Int. Conf. Acoust. Speech and Signal Process., 14-19, 2006.
7. Selesnick, I. W., S. U. Pillai, K. Y. Li, and B. Himed, "Angle-Doppler processing using sparse regularization," Proc. IEEE Int. Conf. Acoust. Speech and Signal Process., 2750-2753, 2010.
8. Sun, K., H. Zhang, G. Li, H. Meng, and X. Wang, "A novel STAP algorithm using sparse recovery technique," Proc. IGARSS, 336-339, 2009.
9. Sun, K., H. Meng, Y. Wang, and X. Wang, "Direct data domain STAP using sparse representation of clutter spectrum," Signal Process., Vol. 91, No. 9, 2222-2236, 2011.
10. Parker, J. T. and L. C. Potter, "A Bayesian perspective on sparse regularization for STAP post-processing," Proc. IEEE Radar Conf, 1471-1475, May 2010.
11. Yang, Z., R. C. de Lamare, and X. Li, "L1-regularized STAP algorithms with a generalized sidelobe canceler architecture for airborne radar," IEEE Trans. on Signal Process., Vol. 60, No. 2, 674-686, 2012.
12. Yang, Z., R. C. de Lamare, and X. Li, "Sparsity-aware STAP algorithms for airborne radar based on conjugate gradient techniques," Proc. Sensor Signal Process. for Defence Conf., London, UK, 2011.
13. Yang, Z., R. C. de Lamare, and X. Li, "L1 regularized STAP algorithm with a generalized sidelobe canceler architecture for airborne radar," Proc. IEEE Workshop on Statist. Signal Process., 329-332, 2011.
14. Liu, Y. and Q. Wan, "Total difference based partial sparse LCMV beamformer," Progress In Electromagnetics Research Letters, Vol. 18, 97-103, 2010.
15. Zhang, Y., Q. Wan, and A.-M. Huang, "Localization of narrow band sources in the presence of mutual coupling via sparse solution finding," Progress In Electromagnetics Research, Vol. 86, 243-257, 2008.
16. Yang, M. and G. Zhang, "Compressive sensing based parameter estimation for monostatic MIMO noise radar," Progress In Electromagnetics Research Letters, Vol. 30, 133-143, 2012.
17. Ke, W. and L. Wu, "Sparsity-based multi-target direct positioning algorithm based on joint-sparse recovery," Progress In Electromagnetics Research C, Vol. 27, 99-114, 2012.
18. Gui, G., N. Zheng, N. Wang, A. Mehbodniya, and F. Adachi, "Compressive estimation of cluster-sparse channels," Progress In Electromagnetics Research C, Vol. 24, 251-263, 2011.
19. Needell, D. and J. Tropp, "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples," Appl. Comp. Harmonic Anal., Vol. 26, 301-321, 2008.
20. Tropp, J. A. and J. Wright, "Computational methods for sparse solution of linear inverse problems," Proc. of IEEE, Vol. 98, No. 6, 948-958, 2010.
21. Wright, S. J., R. D. Nowak, and M. A. T. Figueiredo, "Sparse reconstruction by separable approximation," IEEE Trans. on Signal Process., Vol. 57, No. 7, 2479-2493, 2009.
22. Beck, A. and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse problems," SIAM J. Imag. Sci., Vol. 2, No. 1, 183-202, 2009.
23. Gorodnitsky, I. F. and B. D. Rao, "Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm," IEEE Trans. on Signal Process., Vol. 45, No. 3, 600-616, 1997.
24. 57, 1, "A fast approach for overcomplete sparse decomposition based on smoothed l0 norm," IEEE Trans. on Signal Process., Vol. 57, No. 1, 289-301, 2009.