volume | PIER Journals

Abstract

Traditional synthetic aperture radar (SAR) utilizes Shannon-Nyquist theorem for high bandwidth signal sampling, which induces the complicated system, and it is difficult to transmit and process a huge amount of data caused by high A/D rate. Compressive sensing (CS) indicates that the compressible signal using a few measurements can be reconstructed by solving a convex optimization problem. A novel SAR based on CS theory, named as parallel frequencies SAR (PFSAR), is proposed in this paper. PFSAR transmits a set of narrow bandwidth signals which compose the large total bandwidth. Therefore PFSAR only uses much less data to obtain the same resolution SAR image compared with a traditional SAR system. The data acquisition mode of PFSAR is developed and an algorithm of target scene reconstruction in pursuance of compressive sensing applied to PFSAR is proposed. The azimuth imaging of PFSAR is carried out based on Doppler Effect, and then, the range imaging is performed by using compressive sensing of parallel frequencies signal. Several simulations demonstrate the feasibility and superiority of PFSAR via compressive sensing.

1. Chan, Y. K. and V. C. Koo, "An introduction to synthetic aperture radar (SAR)," Progress In Electromagnetics Research B, Vol. 2, 27-60, 2008.
doi:10.2528/PIERB07110101 Google Scholar

2. Donoho, D., "Compressed sensing," IEEE Transactions on Information Theory, Vol. 52, 1289-1306, 2006.
doi:10.1109/TIT.2006.871582 Google Scholar

3. Candes, E., "Compressive sampling," Proceedings of the International Congress of Mathematicians, 1433-1452, Madrid, Spain, 2006. Google Scholar

4. Baraniuk, R. and P. Steeghs, "Compressive radar imaging," IEEE Radar Conference, 128-133, April 2007.

5. Herman, M. A. and T. Strohmer, "High-resolution radar via compressed sensing," IEEE Transactions on Signal Processing, Vol. 57, 2275-2284, 2009.
doi:10.1109/TSP.2009.2014277 Google Scholar

6. Smith, G. E., T. Diethe, Z. Hussain, J. Shawe-Taylor, and D. R. Hardoon, "Compressed sampling for pulse Doppler radar," IEEE Radar Conference, 887-892, 2010.

7. Bhattacharya, S., T. Blumensath, B. Mulgrew, and M. Davies, "Fast encoding of synthetic aperture radar row data using compressed sensing," IEEE Workshop on Statistical Signal Processing, 448-452, Madison, USA, 2007. Google Scholar

8. Rilling, G., M. Davies, and B. Mulgrew, "Compressed sensing based compression of SAR raw data," Signal Processing with Adaptive Sparse Structured Representations, Saint-Malo(F), April 2009. Google Scholar

9. Tello Alonso, M., P. Lopez-Dekker, and J. Mallorqui, "A novel strategy for radar imaging based on compressive sensing," IEEE International Geoscience and Remote Sensing Symposium, Vol. 2, II-213-II-216, 2008.

10. Tello Alonso, M., P. Lopez-Dekker, and J. Mallorqui, "A novel strategy for radar imaging based on compressive sensing," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 12, 4285-4295, 2010.
doi:10.1109/TGRS.2010.2051231 Google Scholar

11. Lin, Y., Y., Y. Wu, W. Hong, and B. Zhang, "Compressive sensing in radar imaging," IET International Radar Conference, 1-3, 2009.

12. Anitori, L., M. Otten, and P. Hoogeboom, "Compressive sensing for high resolution radar imaging," Proceedings of Asia-Pacific Microwave Conference, 1809-1812, 2010.

13. Mann, S., R. Phogat, and A. K. Mishra, "Dantzig selector based compressive sensing for radar image enhancement," Annual IEEE India Conference, INDICON, 1-4, 2010. Google Scholar

14. Potter, L. C., E. Ertin, J. T. Parker, and M. Cetin, "Sparsity and compressed sensing in radar imaging," Proceedings of the IEEE, Vol. 98, No. 6, 1006-1020, 2010.
doi:10.1109/JPROC.2009.2037526 Google Scholar

15. Ramkrishnan, N., E. Ertin, and R. L. Moses, "Enhancement of coupled multichannel images using sparsity constraints," IEEE Transactions on Image Processing, Vol. 19, No. 8, 2115-2126, 2010.
doi:10.1109/TIP.2010.2045701 Google Scholar

16. Fornasier, M. and H. Rauhut, "Recovery algorithms for vector-valued data with joint sparsity constraints ," SIAM J. Numer. Anal, Vol. 26, No. 2, 577-613, 2008.
doi:10.1137/0606668909 Google Scholar

17. Liang, Q., "Compressive sensing for synthetic aperture radar in fast-time and slow-time domains," Proceedings of the IEEE, 1479-1483, Asilomar, 2011. Google Scholar

18. Xu, J., Y. Pi, and Z. Cao, "Bayesian compressive sensing in synthetic aperture radar imaging," IET Radar, Sonar and Navigation, Vol. 6, No. 1, 2-8, 2012. Google Scholar

19. Samadi, S., M. Cetin, and M. A. Masnadi-Shirazi, "Sparse representation-based synthetic aperture radar imaging," IET Radar, Sonar and Navigation, Vol. 5, No. 2, 182-193, 2011.
doi:10.1049/iet-rsn.2009.0235 Google Scholar

20. 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

21. Li, J., S. S. Zhang and J. F. Chang, "Applications of compressed sensing for multiple transmitters multiple azimuth beams SAR imaging," Progress In Electromagnetics Research, Vol. 127, 259-275, 2012.
doi:10.2528/PIER12021307 Google Scholar

22. Ren, X. Z., Y. F. Li, and R. L. Yang, "Four-dimensional SAR imaging scheme based on compressive sensing," Progress In Electromagnetics Research B, Vol. 39, 225-239, 2012.
doi:10.2528/PIERB11121212 Google Scholar

23. 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.
doi:10.2528/PIER12021307 Google Scholar

24. 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.
doi:10.2528/PIER10080805 Google Scholar

25. 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

26. Ender, J. H. G., "On compressive sensing applied to radar," Signal Processing, Vol. 90, 1402-1414, 2010.
doi:10.1016/j.sigpro.2009.11.009 Google Scholar

27. Candes, E., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, Vol. 52, 489-509, 2006.
doi:10.1109/TIT.2005.862083 Google Scholar

28. Blumensath, T. and M. Davies, "Gradient pursuits," IEEE Transactions on Signal Processing, Vol. 56, 2370-2382, 2008.
doi:10.1109/TSP.2007.916124 Google Scholar

29. Davis, G., S. Mallat, and M. Avellaneda, "Greedy adaptive approximation," Journal of Constructive Approximation, Vol. 12, 57-98, 1997. Google Scholar

30. Donoho, D., M. Elad, and V. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of noise," IEEE Transactions on Information Theory, Vol. 52, 6-18, 2006.
doi:10.1109/TIT.2005.860430 Google Scholar

31. Tropp, J. A. and A. C. Gilbert, "Signal recovery from random measurement via orthogonal matching pursuit," IEEE Transactions on Information Theory, Vol. 53, 4655-4666, 2007.
doi:10.1109/TIT.2007.909108 Google Scholar

Dr. Yanan You

Dr. Huaping Xu

Dr. Chun-Sheng Li

Dr. Lvqian Zhang