SAR processing with zeros

合成孔径雷达(SAR)为军事领域提供了极为宝贵的遥感成像手段，在目标检测中发挥着重要作用。合成孔径雷达的工作原理是测量地面的电磁信号反射。通常使用诸如Polar格式算法的线性频域技术来处理原始接收数据以生成图像。然而，当有缺失的数据时，或者当数据(无论是空间上还是光谱上)中的差距被引入时，这种估计器的性能显著恶化。产生的图像存在失真和混叠现象。最近出现了一种新的信号重建理论，称为压缩感知。它探索了通过包含强信号模型可以在多大程度上使上述不适定抽样问题变得适定。这些技术已经成功地应用于目标检测和超分辨率的SAR图像重建。该方案的主要目的是探索当发射/接收信号中引入空间和/或频率陷波时，压缩感知重建技术在SAR图像形成中的应用。最终，我们希望对SAR数据采集系统可以在不引起严重性能下降的情况下进行如此修改的限制有一个大致的了解。

Synthetic Aperture Radar (SAR) provides the military with an extremely valuable means of remote imaging and plays an important role in target detection. SAR works by measuring the electromagnetic signal reflections from the ground. Processing the raw received data to generate the image is usually performed using linear frequency domain techniqes such as the Polar Format Algorithm. However when there is missing data, or when gaps in the data (either spatially or spectrally) are introduced the performance of such estimators deteriorates considerably. The resulting images exhibit distortion and aliasing artefacts.Recently a new theory for signal reconstruction, called compressed sensing, has emerged. It explores the extent to which ill-posed sampling problems such as those discussed above can be made well-posed through the inclusion of strong signal models. These techniques have already been successfully applied to SAR image reconstruction for target detection and super resolution. The broad aim of this proposal is to explore the application of compressed sensing reconstruction techniques to SAR image formation when spatial and/or frequency notches are introduced into the transmitted/received signals. Ultimately we hope to gain a general understanding of the limits to which the SAR data acquisition system can be so modified without incurring serious performance degradation.