基于稀疏表示的SAR图像属性散射中心参数估计算法

考虑目标频率-方位2维观测数据在属性散射中心模型参数空间上的稀疏性，该文提出一种基于稀疏表示的属性散射中心提取与参数估计方法。由于模型参数维数较高，构造的高维联合字典将消耗较多系统资源。该算法通过分别构建包含位置信息与方位属性参数信息的两个低维字典代替高维的联合字典实现距离特性与方位特性的解耦合，以降低资源需求，并通过正交匹配追踪(OMP)-RELAX联合算法求解l0优化问题，从而实现在频率-方位角域上位置参数与方位属性参数的联合估计。根据提取的属性散射中心可以有效地估计目标或目标重要部件的几何尺寸。基于电磁计算数据和实测数据的实验结果验证了该算法的有效性。

Sparse Representation Based Algorithm for Estimation of Attributed Scattering Center Parameter on SAR imagery

Considering the sparsity of the frequency-aspect backscattered data in the attributed scattering center model parameter domain, a novel method based on sparse representation is proposed to extract attributed scattering center and estimate parameters in frequency-aspect domain. Due to the high dimension of model parameter, one high dimensional joint dictionary needs to be constructed, which may cost a mass storage. In this paper, two low dimensional dictionaries including localization and aspect attribute parameters respectively are constructed to replace the high dimensional joint dictionary to decouple the range characteristic and aspect characteristic, and reduce resource cost; Orthogonal Matching Pursuit (OMP) combined with RELAX are utilized to find the solution of the minimuml0 norm optimization issue and estimate localization parameters and aspect attribute parameters simultaneously. With the extracted attributed scattering centers, geometrical dimensions of the target or its main structure can be estimated. Numerical results both on electromagnetic computation data and measured data verify the validity of the proposed method.