[1] Qiu Xiaohui, , Chen Hao, et al. Entropy function optimization for radar imaging [J]. Journal of Southeast University (English Edition), 2009, 25 (4): 427-430. [doi:10.3969/j.issn.1003-7985.2009.04.002]

Copy

Entropy function optimization for radar imaging()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

25

Issue:

2009 4

Page:

427-430

Research Field:

Electromagnetic Field and Microwave Technology

Publishing date:

2009-12-30

Info

Title:

Entropy function optimization for radar imaging

Author(s):

Qiu Xiaohui¹;  2;  3;  Chen Hao²

¹College of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
²School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221008, China
³ State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China

Keywords:

radar signal processing;  inverse synthetic aperture radar(ISAR)imaging;  auto-focusing

PACS:

TN958;TN957.52

DOI:

10.3969/j.issn.1003-7985.2009.04.002

Abstract:

The convergence performance of the minimum entropy auto-focusing(MEA)algorithm for inverse synthetic aperture radar(ISAR)imaging is analyzed by simulation. The results show that a local optimal solution problem exists in the MEA algorithm. The cost function of the MEA algorithm is not a downward-convex function of multi-dimensional phases to be compensated. Only when the initial values of the compensated phases are chosen to be near the global minimal point of the entropy function, the MEA algorithm can converge to a global optimal solution. To study the optimal solution problem of the MEA algorithm, a new scheme of entropy function optimization for radar imaging is presented. First, the initial values of the compensated phases are estimated by using the modified Doppler centroid tracking(DCT)algorithm. Since these values are obtained according to the maximum likelihood(ML)principle, the initial phases can be located near the optimal solution values. Then, a fast MEA algorithm is used for the local searching process and the global optimal solution can be obtained. The simulation results show that this scheme can realize the global optimization of the MEA algorithm and can avoid the selection and adjustment of parameters such as iteration step lengths, threshold values, etc.

References:

[1] Berizzi F, Corsini G. Autofocusing of inverse synthetic aperture radar images using contrast optimization [J]. IEEE Transactions on Aerospace and Electronic Systems, 1996, 32(3): 1185-1191.
[2] Berizzi F, Dalle Mese E, Martorella M. Performance analysis of a contrast-based ISAR autofocusing algorithm [C]//Proceedings of the 2002 IEEE Radar Conference. Long Beach, CA, USA, 2002: 200-205.
[3] Martorella M, Berizzi F, Bruscoli S. Use of genetic algorithms for ISAR image autofocusing [C]//Proceedings of the 2004 IEEE Radar Conference. Philadelphia, PA, USA, 2004: 201-206.
[4] Li X, Liu G S, Ni J L. Autofocusing of ISAR imaging based on entropy minimization [J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(4): 1240-1251.
[5] Qiu X H, Zhao Y, Chang A H W, et al. Phase compensation in ISAR imaging: comparison between maximum likelihood-based approach and minimum entropy-based approach [C]//IEEE Antennas and Propagation Society International Symposium. Sendai, Japan, 2004: 2107-2110.
[6] Prickett M J, Chen C C. Principle of inverse synthetic aperture radar imaging [C]//Proceedings of EASCON. Arlington, VA, USA, 1980: 340-345.
[7] Chen C C, Andrews H C. Target-motion-induced radar imaging [J]. IEEE Transactions on Aerospace and Electronic System, 1980, 16(1): 2-14.
[8] Qiu X H, Zhao Y, Heng Wang C A, et al. Consistency study of minimum entropy auto-focusing with phase compensation in ISAR imaging [J]. Journal of Electronics & Information Technology, 2007, 29(8): 1799-1801.(in Chinese)
[9] Zhang K H. COPA—a fast and universal phase adjustment for SAR [J]. Journal of Northwestern Polytechnical University, 2008, 26(4): 481-487.(in Chinese)
[10] Yan G F. Function optimization problem based on genetic algorithm and ant algorithm [J]. Journal of Zhejiang University: Engineering Edition, 2007, 41(3): 427-430.(in Chinese)
[11] Zhu Z D, Qiu X H, She Z S. ISAR motion compensation using modified Doppler centroid tracking algorithm [J]. Transactions of Nanjing University of Aeronautics and Astronautics, 1995, 12(2): 1-8.

Memo

Memo:

Biography: Qiu Xiaohui(1968—), female, doctor, associate professor, qiuxh@njupt.edu.cn.
Foundation items: The Natural Science Foundation of Jiangsu Province(No.BK2008429), Open Research Foundation of State Key Laboratory of Millimeter Waves of Southeast University(No.K200903), China Postdoctoral Science Foundation(No.20080431126), Jiangsu Province Postdoctoral Science Foundation(No.2007337).
Citation: Qiu Xiaohui, Chen Hao. Entropy function optimization for radar imaging[J]. Journal of Southeast University(English Edition), 2009, 25(4): 427-430.

Common functions