CAJ | 학술논문

传统相关处理算法不能完全解决宽角合成孔径雷达(WASAR)成像中目标的散射特性随观测角度变化的问题。稀疏信号处理为该问题提供一种新思路，各向异性问题可以建模成欠定方程组。随角度增大，未知量的规模以观测孔径数目的线性规模增长，导致成功重建难度增大，甚至是重建失败。该文提出一种基于信息传递原理的宽角合成孔径雷达成像方法。根据宽角合成孔径雷达的观测几何及目标散射特性在不同角度之间存在的相关性，建立基于结构稀疏的成像模型；然后利用信息传递原理，提出基于结构稀疏复数信息传递(GCAMP)的成像算法求解该成像模型。仿真结果验证了该方法的有效性。
전통상관처리산법불능완전해결관각합성공경뢰체(WASAR)성상중목표적산사특성수관측각도변화적문제。희소신호처리위해문제제공일충신사로，각향이성문제가이건모성흠정방정조。수각도증대，미지량적규모이관측공경수목적선성규모증장，도치성공중건난도증대，심지시중건실패。해문제출일충기우신식전체원리적관각합성공경뢰체성상방법。근거관각합성공경뢰체적관측궤하급목표산사특성재불동각도지간존재적상관성，건립기우결구희소적성상모형；연후이용신식전체원리，제출기우결구희소복수신식전체(GCAMP)적성상산법구해해성상모형。방진결과험증료해방법적유효성。
Conventional matched filtering based algorithms are not sufficiently good at dealing with the anisotropic backscattering behavior of targets in Wide Angle SAR (WASAR) imaging. Sparse signal processing provides a new idea for this problem, the anisotropic problem is modeled as a group of under-determined linear equations. However, the scale of unknowns in the under-determined equations is in linear order of the number of the observation angle. As the observation angle increases, the anisotropic problem becomes more and more difficult to be solved, even failed for conventional sparse signal processing algorithms. This paper presents a Group- sparse Complex Approximated Message Passing (GCAMP) algorithm for WASAR imaging. Firstly, a group sparse based WASAR imaging model is provided according to the structured property of backscattering coefficients across different observation angles. Secondly, the GCAMP algorithm is derived from the imaging model using message passing theory. Results of simulation demonstrate the effectiveness of the proposed algorithm.