volume | PIER Journals

Abstract

In this paper, novel space-time adaptive processing algorithms based on sparse recovery (SR-STAP) that utilize weighted l₁-norm penalty are proposed to further enforce the sparsity and approximate the original l₀-norm. Because the amplitudes of the clutter components from different snapshots are random variables, we design the corresponding weights according to two different ways, i.e., the Capon's spectrum using limited snapshots and the Fourier spectrum using the current snapshot. Moreover, we apply the weighted idea to both the direct data domain (D3) SR-STAP and SR-STAP using multiple snapshots from adjacent target-free range bins. Simulation results illustrate that our proposed algorithms outperform the existing SR-STAP and D3SR-STAP algorithms.

1. Ward, J., "Space-time adaptive processing for airborne radar,", Technical Report 1015, -MIT Lincoln laboratory, Lexington, MAvol, Dec. 1994. Google Scholar

2. Klemm, R., Applications of Space-time Adaptive Processing, The Institution of Electrical Engineers, London, UK, 2004.

3. Guerci, J. R., Space-time Adaptive Processing for Radar, Artech House, 2003.
doi:10.2528/PIER09101502

4. 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.
doi:10.2528/PIERL10092705 Google Scholar

5. Liu, Y. P. and Q. Wan, "Total difference based partial sparse LCMV beamformer," Progress In Electromagnetics Research Letters, Vol. 18, 97-103, 2010.
doi:10.2528/PIER08090703 Google Scholar

6. 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.
doi:10.2528/PIERL12010702 Google Scholar

7. 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. Google Scholar

8. Liu, Z., X. Wei, and X. Li, "Adaptive clutter suppression for airborne random pulse repetition interval radar based on compressed sensing," Progress In Electromagnetics Research, Vol. 128, 291-311, 2012. Google Scholar

9. 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.
doi:10.1109/ICASSP.2010.5496219 Google Scholar

10. 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. Google Scholar

11. 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.
doi:10.1109/TAES.2010.5545209 Google Scholar

12. Li, J., X. Zhu, P. Stoica, and M. Rangaswamy, "High resolution angle-Doppler imaging for MTI radar," IEEE Trans. Aerosp. Electron. Syst., Vol. 46, No. 3, 1544-1556, Jul. 2010. Google Scholar

13. Sun, K., H. Zhang, G. Li, H. Meng, and X. Wang, "A novel STAP algorithm using sparse recovery technique," Proc. IGARSS, 336-339, 2009. Google Scholar

14. Yang, Z., Z. Liu, X. Li, and L. Nie, "Performance analysis of STAP algorithms based on fast sparse recovery techniques," Progress In Electromagnetics Research B, Vol. 41, 251-268, 2012.
doi:10.1016/j.sigpro.2011.04.006 Google Scholar

15. 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.
doi:10.1109/TSP.2011.2172435 Google Scholar

16. Yang, Z., R. C. de Lamare, and X. Li, "L1-regularized STAP algorithms with a generalized sidelobe canceler architecture for airborne radar," IEEE Trans. Signal Process., Vol. 60, No. 2, 674-686, 2012. Google Scholar

17. 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.
doi:10.1007/s00041-008-9045-x Google Scholar

18. Candes, E. J., M. B. Wakin, and S. P. Boyd, "Enhancing sparsity by reweighted l1 minimization," J. Fourier Anal. Applicat., Vol. 14, No. 5-6, 877-905, 2008.
doi:10.1109/LSP.2012.2183592 Google Scholar

19. Xu, X., X. Wei, and Z. Ye, "DOA estimation based on sparse signal recovery utilizing weighted l1-norm penalty," IEEE Signal Process. Letters, Vol. 19, No. 3, 155-158, 2012. Google Scholar

20. , , , http://www.stanford.edu/ boyd/cvx.

Dr. Zhaocheng Yang

Dr. Xiang Li

Dr. Hongqiang Wang