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Deeply Subwavelength Localization with Reverberation-Coded Aperture
Physical Review Letters ( IF 8.1 ) Pub Date : 2021-07-23 , DOI: 10.1103/physrevlett.127.043903
Michael Del Hougne 1 , Sylvain Gigan 2 , Philipp Del Hougne 3
Affiliation  

Accessing subwavelength information about a scene from the far-field without invasive near-field manipulations is a fundamental challenge in wave engineering. Yet it is well understood that the dwell time of waves in complex media sets the scale for the waves’ sensitivity to perturbations. Modern coded-aperture imagers leverage the degrees of freedom (d.o.f.) offered by complex media as natural multiplexor but do not recognize and reap the fundamental difference between placing the object of interest outside or within the complex medium. Here, we show that the precision of localizing a subwavelength object can be improved by several orders of magnitude simply by enclosing it in its far field with a reverberant passive chaotic cavity. We identify deep learning as a suitable noise-robust tool to extract subwavelength localization information encoded in multiplexed measurements, achieving resolutions well beyond those available in the training data. We demonstrate our finding in the microwave domain: harnessing the configurational d.o.f. of a simple programmable metasurface, we localize a subwavelength object along a curved trajectory inside a chaotic cavity with a resolution of λ/76 using intensity-only single-frequency single-pixel measurements. Our results may have important applications in photoacoustic imaging as well as human-machine interaction based on reverberating elastic waves, sound, or microwaves.

中文翻译:

具有混响编码孔径的深度亚波长定位

在没有侵入性近场操作的情况下从远场访问场景的亚波长信息是波浪工程中的一项基本挑战。然而,众所周知,波在复杂介质中的驻留时间决定了波对扰动的敏感度。现代编码孔径成像器利用复杂媒体提供的自由度 (dof) 作为自然多路复用器,但无法识别和获得将感兴趣的对象放置在外部内部之间的根本区别复杂的介质。在这里,我们展示了定位亚波长物体的精度可以提高几个数量级,只需将其用混响无源混沌腔封闭在远场中即可。我们将深度学习确定为一种合适的抗噪工具,可以提取在多路复用测量中编码的亚波长定位信息,实现远远超出训练数据中可用的分辨率。我们展示了我们在微波领域的发现:利用简单可编程超表面的构型自由度,我们沿着混沌腔内的弯曲轨迹定位亚波长物体,分辨率为λ/76使用仅强度的单频单像素测量。我们的结果可能在光声成像以及基于回响弹性波、声音或微波的人机交互中具有重要应用。
更新日期:2021-07-23
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