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针对弹载合成孔径雷达 (SAR) 距离徙动补偿困难以及原始后向投影(BP)算法计算量大实时性差的问题, 本文提出了一种基于解线频调 (dechirp) 弹载SAR的改进BP成像算法. 首先建立导弹在末制导阶段的dechirp回波信号模型并完成距离向的聚焦, 然后对距离向进行条带划分, 结合子孔径合并和图像分裂技术将每个条带内的回波反向投影至成像区域进行相干累加, 从而得到成像区域的SAR图像, 最后分别采用原始BP算法与本文改进BP 算法进行模拟和实测对比实验. 实验结果表明, 该算法能够对目标区域精确成像; 由于各条带间的成像是相互独立的, 易于算法的简化及并行处理, 极大地降低了算法的运算量、提高了运算速度, 增强了算法的工程可实现性. 该研究成果在遥感, 目标的探测与识别, 精确制导等领域中具有重要的应用价值.The range migration of missile borne synthetic apertare radar (SAR) is very difficult to correct using all the conventional imaging algorithms but the back projection algorithm; however, the large computation burden and the low efficiency are the key problems existing in its practical application. To solve the problems, an extended back projection imaging algorithm for the dechirped missile borne SAR is proposed. Firstly, a new signal model is built for the dechirped missile borne SAR, and the range compression is implemented in the range frequency domain. Secondly, the imaging region is divided into several stripes in range direction, and the raw echo data is back projected to each stripe for coherent integration by using the sub-aperture merging and image splitting technique. Finally, the entire SAR image can be obtained by combining the subimages of all the stripes. The validity of the proposed algorithm is demonstrated by the simulated and real SAR datasets. Testing results indicate that the extended algorithm is appropriate for achieving accurate dechirped missile borne SAR image. Moreover, it can be easily parallelized because the stripe imaging is independent of each other, so that can greatly decrease the computation burden, and improve the computation speed. The method introduced in this paper has important theoretical significance in realistic remote sensing, detection and recognition of military targets and precision guide.
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Keywords:
- synthetic aperture radar /
- missile borne /
- dechirp /
- back projection
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[14] Milman A S 1993 Int. J. Remote Sens. 14 1965
[15] Basu S, Bresler Y 2000 IEEE Trans. Image Process. 9 1760
[16] Ulander L M H, Hellsten H, Stenstrom G 2003 IEEE Trans. Aerosp. Electron. Syst. 39 760
[17] Kaplan L M, McClellan J H, Seung M O 2002 IEEE Trans. Aerosp. Electron. Syst. 38 74
[18] Zhu D, Shen M, Zhu Z 2008 IEEE Trans. Geosci. Remote Sens. 46 1579
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[1] Ai W H, Yan W, Zhao X B, Liu W J, Ma S 2013 Acta Phys. Sin. 62 068401 (in Chinese) [艾未华, 严卫, 赵现斌, 刘文俊, 马烁 2013 物理学报 62 068401]
[2] Ji W J, Tong C M 2013 Chin. Phys. B 22 020301
[3] Jiang Z H, Huang S X, Shi H Q, Zhang W, Wang B 2011 Acta Phys. Sin. 60 108402 (in Chinese) [姜祝辉, 黄思训, 石汉青, 张伟, 王彪 2011 物理学报 60 108402]
[4] Cimmino S, Franceschetti G, Iodice A, Riccio D, Ruello G 2003 IEEE Trans. Geosci. Remote Sens. 41 2329
[5] Li J C, Huang B, Peng Y X 2012 Acta Phys. Sin. 61 189501 (in Chinese) [李金才, 黄斌, 彭宇行 2012 物理学报 61 189501]
[6] Yew L N, Wong F H, Cumming I G 2008 IEEE Trans. Geosci. Remote Sens. 46 14
[7] Yeo T S, Tan N L, Zhang C B, Yi H L 2001 IEEE Trans. Geosci. Remote Sens. 39 954
[8] Zhou P, Xiong T, Zhou S, Li Y C, Xing M D 2011 J. Electron. Inf. Tech. 33 622 (in Chinese) [周鹏, 熊涛, 周松, 李亚超, 邢孟道 2011 电子与信息学报 33 662]
[9] Yi Y S, Zhang L R, Liu L, Liu X, Shen D 2009 J. Syst. Eng. Electron. 31 2864 (in Chinese) [易予生, 张林让, 刘楠, 刘昕, 申东 2009 系统工程与电子技术 31 2864]
[10] Clemente C, Soraghan J J 2012 IET Signal Process. 6 503
[11] He C, Liu L Z, Xu L Y, Liu M 2012 IEEE J. STARS 5 1272
[12] Desai M D, Jenkins W K 1992 IEEE Trans. Image Process. 1 505
[13] Chen S, Zhao H C, Zhang S N, Chen Y 2013 Int. J. Digital Content Tech. Appli. 7 323
[14] Milman A S 1993 Int. J. Remote Sens. 14 1965
[15] Basu S, Bresler Y 2000 IEEE Trans. Image Process. 9 1760
[16] Ulander L M H, Hellsten H, Stenstrom G 2003 IEEE Trans. Aerosp. Electron. Syst. 39 760
[17] Kaplan L M, McClellan J H, Seung M O 2002 IEEE Trans. Aerosp. Electron. Syst. 38 74
[18] Zhu D, Shen M, Zhu Z 2008 IEEE Trans. Geosci. Remote Sens. 46 1579
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