Vol. 38
Latest Volume
All Volumes
PIERM 126 [2024] PIERM 125 [2024] PIERM 124 [2024] PIERM 123 [2024] PIERM 122 [2023] PIERM 121 [2023] PIERM 120 [2023] PIERM 119 [2023] PIERM 118 [2023] PIERM 117 [2023] PIERM 116 [2023] PIERM 115 [2023] PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2014-08-22
A Hybrid SAR Autofocus Technique by Two Methods of Sub-Aperture Estimation and Iterative Golden Section Search
By
Progress In Electromagnetics Research M, Vol. 38, 63-71, 2014
Abstract
In a real airborne synthetic aperture radar (SAR), its major phase errors are usually composed of two categories, such as slow-time varying phase errors (less than several cycles of change in phase during synthetic aperture time) and fast-time varying phase errors (otherwise, including wide band random) according to the motion of aircraft. If the fast errors are no more negligible compared to the slow errors, they should be estimated and then compensated accurately to obtain a well focused image. However, it is not proper to estimate all phase errors at the same time like conventional autofocus techniques because the estimation of the fast-time varying phase errors are seriously affected by blurring in image due to the slow-time varying phase errors. In this paper, we presents an accurate hybrid phase estimation technique using two independent estimation stages of sub-aperture and an iterative golden section search method, which has advantages over several existing methods, because of its better estimation accuracy and less sensitive to the quality of extracted range bins as well as requiring less computation time. The performance of our method is illustrated by simulations of point targets and an experiment with real SAR data.
Citation
Boyeon Koh, Sanghyouk Choi, and Joohwan Chun, "A Hybrid SAR Autofocus Technique by Two Methods of Sub-Aperture Estimation and Iterative Golden Section Search," Progress In Electromagnetics Research M, Vol. 38, 63-71, 2014.
doi:10.2528/PIERM14061902
References

1. Samczynski, P. and K. S. Kulpa, "Coherent mapdrift technique," IEEE Transactions on Geosci. Remote Sens., Vol. 48, No. 3, 1505-1517, 2010.
doi:10.1109/TGRS.2009.2032241

2. Xu, J., Y. Peng, and X. G. Xia, "Parametric autofocus of SAR imaging — Inherent accuracy limitations and realization," IEEE Transactions on Geosci. Remote Sens., Vol. 42, No. 11, 2397-2411, 2004.
doi:10.1109/TGRS.2004.837335

3. Wahl, D. E., P. H. Eichel, D. C. Ghiglia, and C. V. Jakowatz Jr., "Phase gradient autofocus --- A robust tool for high resolution phase correction," IEEE Trans. on Aerosp. Electron. Syst., Vol. 30, No. 3, 827-835, 1994.
doi:10.1109/7.303752

4. Van Rossum, W. L., M. P. G. Otten, and R. J. P. Van Bree, "Extended PGA for range migration algorithms," IEEE Trans. on Aerosp. Electron. Syst., Vol. 42, No. 2, 478-488, 2006.
doi:10.1109/TAES.2006.1642565

5. Kolman, J., "PACE: An autofocus algorithm for SAR," Proc. Int. Radar Conf., 310-314, Arlington, VA, 2005.

6. Jakowatz, Jr., C. V. and D. E. Wahl, "Eigenvector method for maximum-likelihood estimation of phase errors in synthetic aperture radar imagery," J. Opt. Soc. Am. A, Vol. 10, No. 12, 2539-2546, 1993.
doi:10.1364/JOSAA.10.002539

7. Ye, W., T. S. Yeo, and Z. Bao, "Weighted least-squares estimation of phase errors for SAR/ISAR autofocus," IEEE Transactions on Geosci. Remote Sens., Vol. 37, No. 5, 2487-2494, 1999.
doi:10.1109/36.789644

8. Cho, K. M. and L. H. Hui, "Autofocus method based on successive parameter adjustments for contrast optimization,", US Patent 7 145 496, Dec. 5, 2006.

9. Fu, T., M. G. Gao, and Y. He, "An improved scatter selection method for phase gradient autofocus algorithm in SAR/ISAR autofocus," IEEE Int. Conf. Neural Network and Signal Processing, 1054-1057, Dec. 2013.

10. Tang, H., H. Shi, and C. Qi, "Study on improvement of phase gradient autofocus algorithm," First Int. Workshop on Education Technology and Computer Science ETCS 2009, Vol. 2, 58-61, 2009.
doi:10.1109/ETCS.2009.275

11. Xi, L., L. Guosui, and J. Ni, "Autofocusing of ISAR images based on entropy minimization," IEEE Trans. on Aerosp. Electron. Syst., Vol. 35, No. 4, 1240-1252, 1999.
doi:10.1109/7.805442

12. Calloway, T. M. and G. W. Donohoe, "Subaperture autofocus for synthetic aperture radar," IEEE Trans. on Aerosp. Electron. Syst., Vol. 30, No. 2, 617-621, 1994.
doi:10.1109/7.272285

13. Wright, N. J. and J. Stagehen, Numerical Optimization, 2nd Edition, Springer, 2006.

14. Wang, J., X. Liu, and Z. Zhou, "Minimum-entropy phase adjustment for ISAR," IEEE Proc. — Radar Sonar Navigat., Vol. 151, No. 4, 203-209, 2004.
doi:10.1049/ip-rsn:20040692

15. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes: The Art of Scientific Computing, 3rd Edition, Cambridge University Press , 2007.

16. Kragh, T. J. and A. A. Kharbouch, "Monotonic iterative algorithms for SAR image reconstruction," IEEE Int. Conf. on Image Processing, 645-648, Oct. 2006.