volume | PIER Journals

Abstract

Space time adaptive processing (STAP) is a signal processing technique for detecting slowly moving targets using airborne radars. The traditional STAP algorithm uses a lot of training cells to estimate the space-time covariance matrix, which occupies large computer memory and is time-consuming. Recently, a number of compressed sensing based STAP algorithms are proposed to detect moving target in strong clutter situation. However, the coherence of the sensing matrix is not low due to the high resolution of the DOA (direction of arrival)-Doppler plane, which does not guarantee a good reconstruction of the sparse vector with large probability. Consequently, the direct estimation of the target amplitude may be unreliable using sparse representation when locating a moving target from the surrounding strong clutter. In this study, a novel method named similar sensing matrix pursuit is proposed to reconstruct the sparse radar scene directly based on the test cell, which reduces the computing complexity efficiently. The proposed method can efficiently cope with the deterministic sensing matrix with high coherence. The proposed method can estimate the weak elements (targets) as well as the prominent elements (clutter) in the DOA-Doppler plane accurately, and distinguish the targets from clutter successfully.

1. Baraniuk, R. and P. Steeghs, "Compressive radar imaging," Proc. Radar Conference, 128-133, 2007. Google Scholar

2. 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

3. Yang, M. and G. Zhang, "Parameter identifiability of monostatic MIMO chaotic radar using compressed sensing," Progress In Electromagnetics Research B, Vol. 44, 367-382, 2012. Google Scholar

4. Liu, J., X. Li, S. Xu, and Z. Zhuang, "ISAR imaging of non-uniform rotation targets with limited pulses via compressed sensing," Progress In Electromagnetics Research B, Vol. 41, 285-305, 2012. Google Scholar

5. Wei, S. J., X.-L. Zhang, J. Shi, and K. F. Liao, "Sparse array microwave 3-D imaging: Compressed sensing recovery and experimental study," Progress In Electromagnetics Research, Vol. 135, 161-181, 2013. Google Scholar

6. Li, J., S. Zhang, and J. Chang, "Applications of compressed sensing for multiple transmitters multiple azimuth beams SAR imaging," Progress In Electromagnetics Research, Vol. 127, 259-275, 2012. Google Scholar

7. Chen, J., J. Gao, Y. Zhu, W. Yang, and P. Wang, "A novel imag formation algorithm for high-resolution wide-swath spaceborne SAR using compressed sensing on azimuth displacement phase center antenna," Progress In Electromagnetics Research, Vol. 125, 527-543, 2012. Google Scholar

8. Wei, S. J., X.-L. Zhang, and J. Shi, "Linear array SAR imaging via compressed sensing," Progress In Electromagnetics Research, Vol. 117, 299-319, 2011. Google Scholar

9. Wei, S. J., X.-L. Zhang, J. Shi, and G. Xiang, "Sparse reconstruction for SAR imaging based on compressed sensing," Progress In Electromagnetics Research, Vol. 109, 63-81, 2010. Google Scholar

10. Gong, Q. and Z.-D. Zhu, "Study stap algorithm on interference target detect under nonhomogenous environment," Progress In Electromagnetics Research, Vol. 99, 211-224, 2009. Google Scholar

11. Tounsi, M. L., R. Touhami, A. Khodja, and M. C. E. Yagoub, "Analysis of the mixed coupling in bilateral microwave circuits including anisotropy for MICS and MMICS applications," Progress In Electromagnetics Research, Vol. 62, 281-315, 2006. Google Scholar

12. Asadi, S. and M. C. E. Yagoub, "Effcient time-domain noise modeling approach for millimeter-wave fets," Progress In Electromagnetics Research, Vol. 107, 129-146, 2010. Google Scholar

13. Habib, M. A., A. Bostani, A. Djaiz, M. Nedil, M. C. E. Yagoub and T. A. Denidni, "Ultra wideband CPW-FED aperture antennawith WLAN band rejection," Progress In Electromagnetics Research, Vol. 106, 17-31, 2010. Google Scholar

14. Guerci, J., Space-time Adaptive Processing for Radar, Artech House, 2003.

15. Klemm, R., "Applications of space-time adaptive processing," Inspec/IEE, 2004. Google Scholar

16. Melvin, W., "A STAP overview ," IEEE Aerospace and Electronic Systems Magazine, Vol. 19, No. 1, 19-35, 2004. Google Scholar

17. Zhang, H., G. Li, and H. Meng, "A class of novel STAP algorithms using sparse recovery technique," Information Theory, 2009. Google Scholar

18. Selesnick, I. W., S. U. Pillai, K. Y. Li, and B. Himed, "Angle-Doppler processing using sparse regularization," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2750-2753, 2010. Google Scholar

19. Parker, J. and L. Potter, "A Bayesian perspective on sparse regularization for STAP post-processing," IEEE Radar Conference, 1471-1475, 2010. Google Scholar

20. Sun, K., H. Meng, and Y.Wang, "Direct data domain STAP using sparse representation of clutter spectrum," Signal Processing, Vol. 91, No. 9, 2222-2236, 2011. Google Scholar

21. Donoho, D. L. and M. Elad, "Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization," Proc. Nat. Acad. Sci., Vol. 100, No. 5, 2197-2202, 2003. Google Scholar

22. Gribonval, R. and M. Nielsen, "Sparse representations in unions of bases," IEEE Trans. on Information Theory, Vol. 49, No. 12, 3320-3325, 2003. Google Scholar

23. Tropp, J. A., "Greed is good: Algorithmic results for sparse approximation," IEEE Trans. on Information Theory, Vol. 50, No. 10, 2231-2224, 2004. Google Scholar

24. Donoho, D., "Compressed sensing ," IEEE Trans. on Information Theory, Vol. 52, No. 4, 1289-1306, 2006. Google Scholar

25. Ben-Haim, Z., Y. C. Eldar, and M. Elad, "Coherence-based performance guarantees for estimating a sparse vector under random noise," IEEE Trans. on Signal Processing, Vol. 58, No. 10, 5030-5043, 2010. Google Scholar

26. Duarte, M. F. and Y. C. Eldar, "Structured compressed sensing: From theory to applications," IEEE Trans. on Signal Processing, Vol. 59, No. 9, 4053-4085, 2011. Google Scholar

27. Dai, W. and O. Milenkovic, "Subspace pursuit for compressive sensing signal reconstruction," IEEE Trans. on Information Theory, Vol. 55, No. 5, 2230-2249, 2009. Google Scholar

28. Stojanovic, I., W. Karl, and M. Cetin, "Compressed sensing of mono-static and multi-static SAR, SPIE defense and security symposium," Algorithms for Synthetic Aperture Radar Imagery XVI, 2009. Google Scholar

29. Joachim , H. G. E., "On compressive sensing applied to radar," Signal Processing, Vol. 90, No. 5, 1402-1414, 2010. Google Scholar

30. Donoho, D. L., M. Elad, and V. N. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of noise," IEEE Trans. on Information Theory, Vol. 52, No. 1, 6-18, 2006. Google Scholar

31. Elad, M., "Optimized projections for compressed sensing," IEEE Trans. on Signal Processing, Vol. 55, No. 12, 5695-5702, 2007. Google Scholar

32. "SparseLab,".
doi:http://dsp.rice.edu/cs Google Scholar

33. Himed, B., Y. Zhang, and A. Hajjari, ",STAP with angle-Doppler compensation for bistatic airborne radars," Proc. of IEEE National Radar Conference, 22-25, 2002. Google Scholar

34. Klemm, R., Principles of Space-time Adaptive Processing, IEE Press, 1999.

Dr. Jing Liu

Dr. Chong Zhao Han

Dr. Xiang Hua Yao

Dr. Feng Lian