Traditional forward-looking imaging methods of real aperture radar yield unsatisfactory performance in the presence of outliers. In this article, a method based on variational Bayesian (VB) is proposed to obtain forward-looking imaging in the presence of outliers. First, considering the non-Gaussian property of the imaging noise due to the outliers, we propose to use the Student- tt distribution to model noise. In this model, the echo signal does not need preprocessing for the outliers. Second, the Laplace hierarchical distribution is introduced to describe the sparsity of the target. Then, the forward-looking imaging problem converts to the optimal problem. Finally, we give the VB derivation to solve the imaging parameter. To illustrate the imaging performance in the presence of outliers, the outliers are randomly added to some angles and the whole scene of the echo signal in the simulations, respectively. From the simulation results, we can see that the proposed method achieves excellent performance for forward-looking imaging in the presence of outliers.

中文翻译：

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存在异常值时基于变分贝叶斯的实孔径雷达前视成像


传统的实孔径雷达前视成像方法在存在异常值的情况下性能不能令人满意。在本文中，提出了一种基于变分贝叶斯（VB）的方法，以在存在异常值的情况下获得前视成像。首先，考虑到异常值导致的成像噪声的非高斯特性，我们建议使用 Student-tt 分布来对噪声进行建模。在该模型中，回波信号不需要对异常值进行预处理。其次，引入拉普拉斯层次分布来描述目标的稀疏性。然后，前视成像问题转化为最优问题。最后给出了求解成像参数的VB推导。为了说明存在异常值时的成像性能，在模拟中将异常值分别随机添加到回波信号的某些角度和整个场景。从仿真结果可以看出，该方法在存在异常值的情况下实现了前视成像的优异性能。