Non-Canonical Integral Transforms for Multistatic Synthetic Aperture Radar (SAR) and Video SAR (VSAR) for Target Motion

这个PHD将生成和验证来自多基地合成孔径雷达(SAR)的动态场景图像形成的创新算法，即VSAR。博士学位将由DSTL和EPSRC iCASE奖联合资助。DSTL的未来传感计划(FSP)寻求在雷达领域推动改变游戏规则的新颖科学技术(S&T)；它开发能够在未来提供运营优势和行动自由的新技术和技术。一个特别令人感兴趣的领域是雷达图像的改进。大多数单站合成孔径雷达成像方案都采用线积分方法。然而，椭圆/椭球、圆形和球面积分变换可以用来对双基地/多基地系统进行数学建模。这与迭代重建和新的正则化方案相结合，将处理复杂的散射现象，产生高分辨率的图像，并提供更多关于场景的信息--例如关于各向异性散射体的信息。这个PHD是考虑动态场景下的SAR成像所必需的。在单基地和双基地合成孔径雷达中，在方位方向上以恒定速度运动的目标会由于每个雷达脉冲之间的位置变化而看起来模糊，而在径向运动的目标会被聚焦，但在方位方向上会出现位移。加速和操纵物体进一步增加了复杂性。将开发理论和实际的数值方法，以了解如何使用多静态数据和其他高维数据来解决这种原本不明确的逆问题

This PhD will generate and validate innovative algorithms for the formation of images of dynamic scenes from multistatic Synthetic Aperture Radar (SAR), i.e. VSAR. The PhD will be jointly funded by Dstl and an EPSRC iCASE Award. Dstl's Future Sensing Programme (FSP) seeks to drive game-changing and novel Science & Technology (S&T) within the radar field; it develops novel technologies and techniques that could deliver operational advantage and freedom of action in the future. One particular area of interest is the improvement of radar imagery. The majority of monostatic SAR image formation schemes utilise a line integral approach. However elliptical/ellipsoidal, circular and spherical integral transforms can be utilised to mathematically model bi-static/multi-static systems. This, coupled with iterative reconstruction and novel regularisation schemes, will tackle complex scattering phenomenology, resulting in fine resolution images and providing more information about the scene - such as about anisotropic scatterers. This PhD is required to consider SAR image formation of dynamic scenes. In mono- and bi-static SAR, objects moving at a constant speed in the azimuth direction will appear blurred due to the change in position between each radar pulse, while those moving in the radial direction will be focussed but appear displaced in azimuth. Accelerating and manoeuvring objects further add to the complexity. Both theoretical and practical numerical methods will be developed to understand how multi-static and other high-dimensional data can be used to resolve this otherwise ambiguous inverse