volume | PIER Journals

Abstract

Sparse regularization imaging method (SRIM) is an effective approach to implement azimuth super-resolution for forward-looking scanning radar. However, for the scene that contains adjacent strong targets in the continuous weak background, SRIM may destroy the structure of the scene when trying to separate the closely located targets. In this paper, a divide and conquer regularization imaging method (DC-RIM) is proposed to solve this problem. Firstly, the data are divided into two channels by the mean-variance segmentation method. Normally, we consider that the data of channel I contain strong scatterers and that the data of channel II contain weak background. Afterwards, SRIM is conducted on channel I to distinguish the targets. For the data of channel II, a region enhancement regularization method is particularly proposed to acquire a good structure of the scene by making use of two-order gradient information of the data. Finally, a good imaging result can be obtained by combining the results of two channels. Experiments based on both synthetic and real data are given to verify the effectiveness of the method.

1. Ma, C., H. Gu, W. Su, and C. Li, "Bistatic forward-looking synthetic aperture radar imaging based on the modified Loffeld’s bistatic formula," Progress In Electromagnetics Research M, Vol. 36, 117-129, 2014.
doi:10.2528/PIERM14031706

2. Liu, C., S. Zhang, C. Dai, and J. Zhou, "Focusing translational variant bistatic forward-looking SAR data based on two-dimensional non-uniform FFT," Progress In Electromagnetics Research M, Vol. 37, 1-10, 2014.
doi:10.2528/PIERM14040501

3. Zhang, Y., Y. Huang, Y. Zha, and J. Yang, "Superresolution imaging for forward-looking scanning radar with generalized gaussian constraint," Progress In Electromagnetics Research M, Vol. 46, 1-10, 2016.
doi:10.2528/PIERM15120805

4. Loehner, A., "Improved azimuthal resolution of forward looking SAR by sophisticated antenna illumination function design," IEE Proceedings - Radar, Sonar and Navigation, Vol. 145, No. 2, 128-134, 1998.
doi:10.1049/ip-rsn:19981731

5. Feng, D., D. X. An, and X.-T. Huang, "Image formation using fast factorized backprojection based on sub-aperture and sub-image for general bistatic forward-looking SAR with arbitrary motion," Progress In Electromagnetics Research B, Vol. 74, 141-153, 2017.
doi:10.2528/PIERB17011702

6. Dropkin, H. and C. Ly, "Superresolution for scanning antenna," 1997 IEEE National Radar Conference, 306-308, IEEE, 1997.
doi:10.1109/NRC.1997.588326

7. Zhang, Y., Y. Zhang, W. Li, Y. Huang, and J. Yang, "Super-resolution surface mapping for scanning radar: Inverse filtering based on the fast iterative adaptive approach," IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 1, 127-144, 2017.
doi:10.1109/TGRS.2017.2743263

8. Zha, Y., Y. Huang, and J. Yang, "An iterative shrinkage deconvolution for angular superresolution imaging in forward-looking scanning radar," Progress In Electromagnetics Research B, Vol. 65, 35-48, 2016.
doi:10.2528/PIERB15100501

9. Golub, G. H., P. C. Hansen, and D. P. O’Leary, "Tikhonov regularization and total least squares," SIAM Journal on Matrix Analysis and Applications, Vol. 21, No. 1, 185-194, 1999.
doi:10.1137/S0895479897326432

10. Moulin, P., "A wavelet regularization method for diffuse radar-target imaging and speckle-noise reduction," Journal of Mathematical Imaging and Vision, Vol. 3, No. 1, 123-134, 1993.
doi:10.1007/BF01248407

11. Zhang, X., E. Y. Lam, E. X. Wu, and K. K. Wong, "Application of Tikhonov regularization to super-resolution reconstruction of brain MRI images," Medical Imaging and Informatics, 51-56, 2008.
doi:10.1007/978-3-540-79490-5_8

12. Liu, L., W. Huang, and C. Wang, "Texture image prior for SAR image super resolution based on total variation regularization using split Bregman iteration," International Journal of Remote Sensing, Vol. 38, No. 20, 5673-5687, 2017.
doi:10.1080/01431161.2017.1346325

13. Zhu, X. X. and R. Bamler, "Tomographic sar inversion by l1-norm regularization - The compressive sensing approach," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 10, 3839-3846, 2010.
doi:10.1109/TGRS.2010.2048117

14. 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

15. Zhang, L., M. Xing, C.-W. Qiu, J. Li, and Z. Bao, "Achieving higher resolution ISAR imaging with limited pulses via compressed sampling," IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 3, 567-571, 2009.
doi:10.1109/LGRS.2009.2021584

16. Chen, H. M., M. Li, Z. Wang, Y. Lu, P. Zhang, and Y. Wu, "Sparse super-resolution imaging for airborne single channel forward-looking radar in expanded beam space via lp regularisation," Electronics Letters, Vol. 51, No. 11, 863-865, 2015.
doi:10.1049/el.2014.3978

17. Guan, J., J. Yang, Y. Huang, and W. Li, "Maximum a posteriori-based angular superresolution for scanning radar imaging," IEEE Transactions on Aerospace and Electronic Systems, Vol. 50, No. 3, 2389-2398, 2014.
doi:10.1109/TAES.2014.120555

18. Hansen, P. C. and D. P. O’Leary, "The use of the l-curve in the regularization of discrete ill-posed problems," SIAM Journal on Scientific Computing, Vol. 14, No. 6, 1487-1503, 1993.
doi:10.1137/0914086

19. Tan, K., W. Li, Y. Huang, and J. Yang, "Angular resolution enhancement of real-beam scanning radar base on accelerated iterative shinkage/thresholding algorithm," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 929-932, IEEE, 2016.
doi:10.1109/IGARSS.2016.7729235

20. Zha, Y., Y. Huang, Z. Sun, Y. Wang, and J. Yang, "Bayesian deconvolution for angular superresolution in forward-looking scanning radar," Sensors, Vol. 15, No. 3, 6924-6946, 2015.
doi:10.3390/s150306924

Dr. Ke Tan

Dr. Wenchao Li

Dr. Yulin Huang

Dr. Qian Zhang

Dr. Jianyu Yang