volume | PIER Journals

Abstract

Circular synthetic aperture radar (CSAR) imaging based on compressive sensing with random step frequency (RSF) as transmitted signal is introduced. CSAR is capable of obtaining both two-dimensional high resolution image and three-dimensional image due to a circular collection trajectory. RSF signal shares good characteristics of noise signals including ``thumbtack-shape" ambiguity function, low probability of interception, and strong anti-jamming capability. As a result, CSAR adopting RSF signal can make use of advantages of both CSAR and RSF signal. Compressive sensing is a new data acquisition and reconstruction theorem for sparse or compressible signals, which needs fewer samples to reconstruct signals than traditional Nyquist theorem. Simulation results show that both two-dimensional and three-dimensional targets can be well reconstructed from few samples by applying compressive sensing to RSF CSAR imaging.

1. Soumekh, M., Synthetic Aperture Radar Signal Processing with MATLAB Algorithms, Wiley, New York, 1999.

2. Bryant, M. L., L. L. Gostin, and M. Soumekh, "Three-dimensional E-CSAR imaging of a T-72 tank and synthesis of its spotlight, stripmap and interferometric SAR reconstructions," International Conference on Image Processing, 628-631, 2001. Google Scholar

3. Bryant, M. L., L. L. Gostin, and M. Soumekh, "3-D E-CSAR maging of a T-72 tank and synthesis of its SAR reconstructions," IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, No. 1, 211-227, 2003.
doi:10.1109/TAES.2003.1188905 Google Scholar

4. Cantalloube, H. M. J., P. Dubois-Fernandez, and X. Dupuis, "Very high resolution SAR images over dense urban area," IEEE International Geoscience and Remote Sensing Symposium, Vol. 4, 2799-2802, 2005. Google Scholar

5. Cantalloube, H. and E. C. Koeniguer, "Assessment of physical limitations of high resolution on targets at X-band from circular SAR experiments," European Conference on Synthetic Aperture Radar, June 2008. Google Scholar

6. Oriot, H. and H. Cantalloube, "Circular SAR imagery for urban remote sensing," European Conference on Synthetic Aperture Radar, June 2008. Google Scholar

7. Palm, S. and H. Oriot, "DEM extraction over urban area using circular SAR imagery," European Conference on Synthetic Aperture Radar, June 2008. Google Scholar

8. Frölind, P.-O. U., M. H. Lars, and A. Gustavsson, "First results on VHF-band SAR imaging using circular tracks," European Conference on Synthetic Aperture Radar, June 2008. Google Scholar

9. Tan, W. X., Y. P.Wang, H.Wen, et al. "Circular SAR experiment for human body imaging," Asian and Pacific Conference on Synthetic Aperture Radar, 90-93, 2007.
doi:10.1109/APSAR.2007.4418562 Google Scholar

10. Tan, W. X., W. Hong, Y. P. Wang, and Y. R. Wu, "A novel spherical-wave three-dimensional imaging algorithm for microwave cylindrical scanning geometries," Progress In lectromagnetics Research, Vol. 111, 43-70, 2011.
doi:10.2528/PIER10100307 Google Scholar

11. Axelsson, S. R. J., "Random noise radar/sodar with ultrawide-band waveforms," IEEE Transactions on Geoscience and Remote Sensing , Vol. 45, No. 5, 1099-1114, 2007.
doi:10.1109/TGRS.2007.893742 Google Scholar

12. Axelsson, S. R. J., "Analysis of random step frequency radar and comparison with experiments," IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 4, 890-904, 2007.
doi:10.1109/TGRS.2006.888865 Google Scholar

13. Candes, E. J., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, Vol. 52, No. 2, 489-509, 2006.
doi:10.1109/TIT.2005.862083 Google Scholar

14. Donoho, D. L., "IEEE Transactions on Information Theory,", Vol. 52, No. 4, 1289-1306, 2006.
doi:10.1109/TIT.2006.871582 Google Scholar

15. Candes, E. J. and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?," IEEE Transactions on Information Theory, Vol. 52, No. 2, 5406-5425, 2006.
doi:10.1109/TIT.2006.885507 Google Scholar

16. Baraniuk, R. and P. Steeghs, "Compressive radar imaging," IEEE Radar Conference, 128-133, 2007.
doi:10.1109/RADAR.2007.374203 Google Scholar

17. Zhu, X. X. and R. Bamler, "Compressive sensing for high resolution differential SAR tomography-the SL1MMER algorithm," IEEE International Geoscience and Remote Sensing Symposium, 17-20, 2010. Google Scholar

18. 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

19. 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

20. Liu, Z., X. Z. Wei, and X. Li, "Adaptive clutter suppression for airborne random pulse repretition interval radar based on compressed sensing," Progress In Electromagnetics Research, Vol. 128, 291-131, 2012. Google Scholar

21. Lin, Y., W. Hong, W.-X. Tan, et al. "Compressed sensing technique for circular SAR imaging," IET International Radar Conference, April 2009. Google Scholar

22. Chen, S. S., D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM Journal on Scientific Computing, Vol. 20, No. 1, 33-61, 1998.
doi:10.1137/S1064827596304010 Google Scholar

23. Birgin, E. G., J. M. Martínez, and M. Raydan, "Nonmonotone spectral projected gradient methods on convex sets," SIAM Journal on Optim., Vol. 10, No. 4, 1196-1211, 2000.
doi:10.1137/S1052623497330963 Google Scholar

Dr. Lingjuan Yu

Prof. Yunhua Zhang