摘要
针对稀疏孔径条件下双基地ISAR成像分辨率低、运算时间长等问题,提出了一种基于快速稀疏贝叶斯学习的高分辨成像算法。首先,建立基于压缩感知的双基地ISAR稀疏孔径回波模型,然后将整个二维回波数据进行分块处理,并假设目标图像各像元服从高斯先验,建立稀疏贝叶斯模型,再利用快速边缘似然函数最大化方法求解得到高质量目标图像,最后将所求的每块回波对应的目标图像合成整个二维图像。由于采取了分块处理,在每块图像重构时减少了数据存储量和计算量。另外,相比于传统的稀疏贝叶斯学习求解方法,本文所提快速算法在保证重构质量的同时进一步缩短了运算时间,仿真实验验证了算法的有效性和优越性。
Aiming at solving the problems of energy leakage and low resolution in bistatic inverse synthetic aperture radar(ISAR)imaging with sparse apertures,a high-resolution imaging algorithm based on fast sparse Bayesian learning(SBL)is proposed in this paper.First,the sparse echo model is established based on the compressive sensing theory.Then the whole two-dimensional data is divided into several blocks,and assuming that each pixel of the target image obeys Gaussian prior to establish the SBL model.Then the fast marginal likelihood maximization method is used to reconstruct the target image of each block.Finally,the whole image is synthesized by the target image corresponding to each block echo.Due to the block processing,the data storage and computation are reduced in each block image reconstruction.In addition,compared with the traditional SBL methods,the proposed fast method guarantees the quality of reconstruction while shortening the computation time.The validity and superiority of the algorithm are verified by the simulation experiments.
引文
[1]保铮,邢孟道,王彤.雷达成像技术[M].北京:电子工业出版社,2005.
[2]杨振起,张永顺,骆永军.双(多)基地雷达系统[M].北京:国防工业出版社,1998.
[3]CATALDO D,MARTORELLA M.SuperResolution for Bistatic Distortion Mitigation[C]∥IEEE Radar Conference,Philadelphia,PA:IEEE,2016:1-6.
[4]李少东,陈文峰,杨军,等.稀疏孔径下的运动补偿及快速超分辨成像方法[J].电子学报,2017,45(2):291-299.
[5]ZHANG Shunsheng,ZHANG Wei,ZONG Zhulin,et al.High-Resolution Bistatic ISAR Imaging Based on TwoDimensional Compressed Sensing[J].IEEE Trans on Antennas and Propagation,2015,63(5):2098-2111.
[6]郭宝锋,尚朝轩,王俊岭,等.双基地角时变下的逆合成孔径雷达越分辨单元徙动校正算法[J].物理学报,2014,63(23):238406.
[7]GUO Baofeng,WANG Junling,GAO Meiguo,et al.Research on Spatial-Variant Property of Bistatic ISAR Imaging Plane of Space Target[J].Chinese Physics B,2015,24(4):048402.
[8]XU Gang,XING Mengdao,XIA Xianggen,et al.High-Resolution Inverse Synthetic Aperture Radar Imaging and Scaling with Sparse Aperture[J].IEEEJournal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(8):4010-4027.
[9]钱宇雷.基于压缩感知的雷达成像方法研究[D].南京:南京航空航天大学,2016.
[10]ZHANG Lei,QIAO Zhijun,XING Mengdao,et al.High-Resolution ISAR Imaging by Exploiting Sparse Apertures[J].IEEE Trans on Antennas and Propagation,2012,60(2):997-1008.
[11]LIU Hongchao,JIU Bo,LIU Hongwei,et al.Superresolution ISAR Imaging Based on Sparse Bayesian Learning[J].IEEE Trans on Geoscience and Remote Sensing,2014,52(8):5005-5013.
[12]BAE J,KAE B,LEE S,et al.Bistatic ISAR Image Reconstruction Using Sparse-Recovery Interpolation檾檾檾檾檾檾檾檾檾檾檾檾檾檾檾檾檾檾檾檾檾檾檾of Missing Data[J].IEEE Trans on Aerospace and E-lectronic Systems,2016,52(3):1155-1167.
[13]TIPPING M E.Sparse Bayesian Learning and the Relevance Vector Machine[J].Journal of Machine Learning Research,2001,1:211-244.
[14]TIPPING M E,FAUL A C.Fast Marginal Likelihood Maximisation for Sparse Bayesian Models[C]∥Ninth International Workshop on Artificial Intelligence and Statistics,Key West,FL:[s.n.],2003:1-13.
[15]ZHANG Lei,WANG Hongxian,QIAO Zhijun.Resolution Enhancement for ISAR Imaging via Improved Statistical Compressive Sensing[J].EURASIP Journal on Advances in Signal Processing,2016,2016[80]:1-19.
