基于块稀疏贝叶斯学习的SAR图像目标方位角估计方法

提出了一种基于块稀疏贝叶斯学习的合成孔径雷达（Synthetic aperture radar，SAR）图像目标方位角估计方法。SAR图像具有较强的方位角敏感性，因此对于具有某一方位角的SAR图像仅能与其具有相近方位角的样本具有较高的相关性。方法基于稀疏表示的基本思想，首先对所有训练样本按照方位角顺序排列为全局字典。在此条件下，待估计样本在该字典上的线性表示系数具有块稀疏特性，即非零表示系数主要聚集在字典上的某一局部区域。求解得到的块稀疏位置包含的训练样本可以有效地反映待估计样本的方位角信息。采用块稀疏贝叶斯学习（Block sparse Bayesian learning, BSBL）算法求解全局字典上的稀疏表示系数，并根据具有最小重构误差的原则获得最佳的局部分块。在获取最佳分块的基础上，方位角计算方法采用线性加权的方式综合了该分块区间内所有训练样本的方位角信息从而获得更为稳健的估计结果。所提出的方法在充分考察SAR图像方位角敏感性的基础上，综合运用局部区间内样本的有效信息，避免了基于单一样本估计的不确定性。为了验证所提出方法的有效性，基于Moving and stationary target acquisition and recognition （MSTAR）数据集进行了方位角估计实验并与几类经典方法进行对比分析。实验结果验证了所提出方法的性能优势。

Target azimuth estimation of synthetic aperture radar image based on block sparse Bayesian learning

A target azimuth estimation algorithm of Synthetic Aperture Radar (SAR) images based on block sparse Bayesian learning was proposed. SAR images were highly sensitive to target azimuth, the SAR image with a special azimuth only highly correlate with those samples with approaching azimuths. The proposed method was developed based on the idea of sparse representation. First, all the training samples were sorted according to the azimuths to construct the global dictionary. Then, the sparse coefficients of test sample to be estimated over the global dictionary should be block sparse ones, that was the non-zero coefficients mainly accumulate in a local part on the global dictionary. The solved positions of the blocks effectively reflect the azimuthal information of the test sample. The block sparse Bayesian learning (BSBL) algorithm was employed to solve the block sparse coefficients and then the candidate blocks were chosen based on the minimum of the reconstruction errors. With the optimal block, the estimated azimuth was calculated by linearly fusing the azimuths of all the training samples in the block thus a robust estimation result could be achieved. The proposed method considered the azimuthal sensitivity of SAR images and comprehensively utilized the valid information in a local discretionary, so the instability of using a signal reference training sample could be avoided. Experiments were conducted on moving and stationary target acquisition and recognition (MSTAR) dataset to validate effectiveness of the proposed method while compared with several classical algorithms. The experimental results validate the superior performance of the proposed method.