摘要
目的针对基于压缩感知(CS)的逆合成孔径雷达(ISAR)成像方法的成像质量和应用一直受到目标场景稀疏性好坏和迭代重建耗时长限制的问题,提出一种基于交替方向乘子法网络(ADMMN)的ISAR成像方法。方法根据交替方向乘子法(ADMM)求解稀疏假设下CS ISAR成像模型时采取的分裂变量的策略,将凸优化迭代求解过程映射到一个多级的深度神经网络,构建出ADMMN。ADMMN通过训练学习欠采样的ISAR测量数据与高质量目标图像之间的映射关系,借此实现ISAR欠采样数据成像。结果实验采用仿真卫星数据和实测飞机数据,两种数据的采样率分别为25%和10%。实验结果表明,相较于典型的CS ISAR正交匹配追踪(OMP)成像方法和贪婪卡尔曼滤波(GKF)成像方法,ADMMN成像方法能够更准确地重建目标区域散射点,在虚警(FA)、漏检(MD)和相对均方根误差(RRMSE)等成像质量评估指标上均有改善。在卫星数据成像实验中,相比于OMP和GKF,ADMMN在RRMSE指标上分别降低了49. 8%和26. 5%。在飞机数据成像实验中,相比于OMP和GKF,ADMMN在RRMSE指标上分别降低了68. 7%和74. 9%。此外,在验证ADMMN先验信息依赖性的实验中,分别采用卫星训练数据和飞机训练数据训练好的两种ADMMN,都能够对10%的飞机目标测量数据成像。结论融合深度学习和凸优化迭代求解策略的ADMMN ISAR成像方法能够使用非常少的数据获得高质量的成像结果,且成像效率高。
Objective Traditional inverse synthetic aperture radar( ISAR) imaging uses the range-Doppler( RD) method.Compressive sensing( CS)-based ISAR imaging method that appeared in the last decade can obtain imaging results with high image contrast( IC) and minimal sidelobe interference using few undersampled data. However,the imaging quality and application of the CS ISAR imaging method are limited by the performance of the sparse representation of the target scene and the time-consuming iteration reconstruction. An alternating direction method of multipliers network( ADMMN)-based ISAR imaging method is proposed in this study to improve the image reconstruction quality and efficiency of CS ISAR imaging. Method ADMMN is a model-driven deep neural network( MDDNN) constructed by mapping the iterative steps of the alternating direction method of multipliers( ADMM) algorithm into the architecture of MDDNN. This network architecture can be explicitly expressed in terms of polynomials,which facilitate the generation of an accurate imaging network. The convex optimizing iterative solution process is mapped to a multi-level deep neural network( DNN) according to the strategy of splitting variables adopted by the ADMM algorithm to solve a CS ISAR imaging model under sparse assumption and construct the ADMMN. The network consists of four hidden layers,namely,reconstruction layer,transformation dictionary layer,nonlinear transformation layer,and multiplier update layer. The reconstruction layer is used for ISAR image reconstruction,the transformation dictionary layer is used to extract the sparse representation of the ISAR image,the nonlinear transformation layer is used to obtain the nonlinear characteristics of the ISAR image,and the multiplier update layer is used to update the Lagrange multiplier. ADMMN is trained to learn the mapping relationship between undersampled ISAR measurements and high-quality target images to realize ISAR undersampled data imaging. The target image is the well-focused ISAR image obtained by performing
作者
李泽
汪玲
胡长雨
Li Ze;Wang Ling;Hu Changyu(Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《中国图象图形学报》
CSCD
北大核心
2019年第11期2045-2056,共12页
Journal of Image and Graphics
基金
国家自然科学基金项目(61871217)
江苏省研究生科研与实践创新计划项目(KYCX18_0291)~~
关键词
成像
压缩感知
逆合成孔径雷达
凸优化
深度神经网络
深度交替方向乘子法网络
imaging
compressive sensing(CS)
inverse synthetic aperture radar(ISAR)
convex optimization
deep neural network(DNN)
deep alternating direction method of multipliers network(ADMMN)
