光学学报, 2017, 37 (6): 0611003, 网络出版: 2017-06-08
基于贝叶斯学习的下视三维合成孔径雷达成像方法 下载: 552次
Imaging Method of Downward-Looking Three-Dimensional Synthetic Aperture Radar Based on Bayesian Learning
成像系统 合成孔径雷达 三维成像 贝叶斯学习 Lp正则化 imaging systems synthetic aperture radar three-dimensional imaging Bayesian learning Lp regularization
摘要
为了获得理想的跨航向分辨率, 现有下视三维合成孔径雷达(DL 3D SAR)成像方法所需天线阵列过长, 且阵元数目过多。针对该问题, 提出了一种基于Lp正则化的DL 3D SAR成像方法。在分析DL 3D SAR回波信号模型的基础上, 构建超完备字典, 将跨航向成像过程转化为Lp范数最小化问题, 并分析其可行性, 最后使用稀疏贝叶斯学习方法对其进行优化求解以获得最终的成像结果。仿真实验结果表明, 该方法在保证成像质量的前提下可以将成像所需阵列长度减少为原长度的1/4, 或者在相同阵列条件下将跨行向分辨率提高1倍。
Abstract
To meet the request of high resolution on cross-track, conventional downward-looking three-dimensional synthetic aperture radar (DL 3D SAR) imaging on micro unmanned aerial vehicle requires much longer transmitting antenna and more receiving antenna array. A novel imaging method of DL 3D SAR based on Lp regularization is proposed. Analyzing the 3D echo signal model, the over-complete dictionary is structured. And the imaging problem is transformed into a Lp regularization model which can be solved by sparse Bayesian learning method. The simulation results show that the proposed method can cut down nearly 3/4 length of antenna array without reducing the imaging quality obviously, or make the cross-track resolution improve 2 times with full sampling compared to the conventional method.
康乐, 张群, 李涛泳, 顾福飞. 基于贝叶斯学习的下视三维合成孔径雷达成像方法[J]. 光学学报, 2017, 37(6): 0611003. Kang Le, Zhang Qun, Li Taoyong, Gu Fufei. Imaging Method of Downward-Looking Three-Dimensional Synthetic Aperture Radar Based on Bayesian Learning[J]. Acta Optica Sinica, 2017, 37(6): 0611003.