CISS Department, Royal Military Academy, 30 Av. de la Renaissance, B-1000 Brussels, Belgium.
ETRO Department, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium.
Sensors (Basel). 2018 May 31;18(6):1761. doi: 10.3390/s18061761.
This work explores an innovative strategy for increasing the efficiency of compressed sensing applied on mm-wave SAR sensing using multiple weighted side information. The approach is tested on synthetic and on real non-destructive testing measurements performed on a 3D-printed object with defects while taking advantage of multiple previous SAR images of the object with different degrees of similarity. The tested algorithm attributes autonomously weights to the side information at two levels: (1) between the components inside the side information and (2) between the different side information. The reconstruction is thereby almost immune to poor quality side information while exploiting the relevant components hidden inside the added side information. The presented results prove that, in contrast to common compressed sensing, good SAR image reconstruction is achieved at subsampling rates far below the Nyquist rate. Moreover, the algorithm is shown to be much more robust for low quality side information compared to coherent background subtraction.
这项工作探索了一种创新策略，通过使用多个加权侧信息来提高应用于毫米波 SAR 传感的压缩感知效率。该方法在合成和真实无损检测测量中进行了测试，这些测量是在具有缺陷的 3D 打印物体上进行的，同时利用了该物体具有不同相似程度的多个先前 SAR 图像。测试的算法在两个级别上自动为侧信息分配权重：（1）在侧信息内部的组件之间，以及（2）在不同的侧信息之间。因此，通过利用添加的侧信息内部隐藏的相关组件，重建几乎不受质量差的侧信息的影响。所呈现的结果证明，与常见的压缩感知相比，在远低于奈奎斯特率的采样率下就能实现良好的 SAR 图像重建。此外，与相干背景相减相比，该算法对于低质量的侧信息表现出更强的稳健性。
