2016-09-23
Segment Noncoherent Integration Based Inverse Synthetic Aperture Radar Imaging Under Low Signal-to-Noise Ratio
By
Progress In Electromagnetics Research M, Vol. 50, 105-115, 2016
Abstract
In this paper, a novel scheme for inverse synthetic aperture radar (ISAR) imaging under low signal-to-noise ratio (SNR) condition is proposed. The method is a preprocess of the high-resolution range profiles and relies on the oversampling in the azimuth direction. It divides the entire coherent processing interval into segments according to the down sampling factor. In each segment, original low SNR echoes are noncoherently integrated to obtain a new high SNR echo. With the new high SNR echoes, conventional methods for ISAR imaging can perform much better and obtain a better focused ISAR image. The presented algorithm has the advantage of effectiveness under low SNR condition and computational efficiency. Experimental results based on both the simulated and real radar data of an airplane verify the superiority of the proposed strategy.
Citation
Jianzhi Lin, Yue Zhang, Weixing Li, and Zeng Ping Chen, "Segment Noncoherent Integration Based Inverse Synthetic Aperture Radar Imaging Under Low Signal-to-Noise Ratio," Progress In Electromagnetics Research M, Vol. 50, 105-115, 2016.
doi:10.2528/PIERM16062305
References

1. Chen, C. C. and H. C. Andrews, "Target-motion-induced radar imaging," IEEE Transactions on Aerospace and Electronic Systems, Vol. 16, No. 1, 2-14, 1980.
doi:10.1109/TAES.1980.308873        Google Scholar

2. Delise, G. Y. and H. Wu, "Moving target imaging and trajectory computation using ISAR," IEEE Transactions on Aerospace and Electronic Systems, Vol. 30, No. 3, 887-889, 1994.
doi:10.1109/7.303757        Google Scholar

3. Wang, J. and D. Kasilingam, "Global range alignment for ISAR," IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, No. 1, 351-357, 2003.
doi:10.1109/TAES.2003.1188917        Google Scholar

4. Wang, J. and X. Liu, "Improved global range alignment for ISAR," IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 3, 1070-1075, 2007.
doi:10.1109/TAES.2007.4383594        Google Scholar

5. Zhu, D., L. Wang, Y. Yu, Q. Tao, and Z. Zhu, "Robust ISAR range alignment via minimizing the entropy of the average range profile," IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 2, 204-208, 2009.
doi:10.1109/LGRS.2008.2010562        Google Scholar

6. Itoh, T. M. and G. W. Donohoe, "Motion compensation for ISAR via centroid tracking," IEEE Transactions on Aerospace and Electronic Systems, Vol. 32, No. 7, 1191-1197, 1996.
doi:10.1109/7.532283        Google Scholar

7. Ye, W., T. S. Yeo, and Z. Bao, "Weighted least-squares estimation of phase errors for SAR/ISAR autofocus," IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 9, 2487-2494, 1999.
doi:10.1109/36.789644        Google Scholar

8. Eichel, P. H. and C. V. Jakowatz, "Phase-gradient algorithm as an optimal estimator of the phase derivative," Optics Letters, Vol. 14, No. 20, 1101-1103, 1989.
doi:10.1364/OL.14.001101        Google Scholar

9. Huang, D. R., L. Zhang, M. D. Xing, and Z. Bao, "ISAR autofocus method for maneuvering targets," Journal of Xidian University, Vol. 41, No. 3, 71-78, 2014.        Google Scholar

10. Li, X., G. Liu, and J. Ni, "Autofocusing of ISAR imaging based on entropy minimization," IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 4, 1240-1251, 1999.
doi:10.1109/7.805442        Google Scholar

11. Martorella, M., F. Berizzi, and B. Haywood, "Contrast maximization based technique for 2-D ISAR autofocusing," IEE Proceedings on Radar, Sonar and Navigation, Vol. 52, No. 4, 253-262, 2005.
doi:10.1049/ip-rsn:20045123        Google Scholar

12. Martorella, M., F. Berizzi, and S. Bruscoli, "Use of genetic algorithms for contrast and entropy optimization in ISAR autofocusing," EURASIP Journal on Applied Signal Processing, Vol. 2006, No. 87298, 1-11, 2006.        Google Scholar

13. Yang, L., T. Xiong, L. Zhang, and M. D. Xing, "Translational motion compensation for ISAR imaging based on joint autofocusing under the low SNR," Journal of Xidian University, Vol. 39, No. 3, 63-71, 2012.        Google Scholar

14. Zhang, L., J. L. Sheng, J. Duan, M. D. Xing, Z. J. Qiao, and Z. Bao, "Translational motion compensation for ISAR imaging under low SNR by minimum entropy," EURASIP Journal on Advances in Signal Processing, Vol. 2013, No. 33, 1-19, 2013.        Google Scholar

15. Liu, L., F. Zhou, M. L. Tao, P. G. Sun, and Z. J. Zhang, "Adaptive translational motion compensation method for ISAR imaging under low SNR based on particle swarm optimization," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 11, 5146-5157, 2015.
doi:10.1109/JSTARS.2015.2491307        Google Scholar

16. Zhang, S. H., Y. X. Liu, and X. Li, "Pseudomatched-filter-based ISAR imaging under low SNR condition," IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 7, 1240-1244, 2014.
doi:10.1109/LGRS.2013.2290541        Google Scholar