面向空间域感知的自适应量子加速成像
Adaptive quantum accelerated imaging for space domain awareness
AI总结：
提出量子加速成像（QAI）方法应对空间域感知挑战，在极端亮度对比和多目标场景下显著提升目标检测、定位和分辨率，性能优于传统直接成像。
摘要：
空间活动的增长增加了对空间域感知（SDA）的需求，以确保太空安全运行。然而，对太空目标进行成像和检测具有挑战性，原因在于它们外观暗淡、角尺寸/间距小、分布密集，以及大气湍流。。这些挑战使得地基成像观测中的太空目标表现为亚瑞利区域中的点状物体，具有极端亮度对比但光子预算低。在此，我们提出使用近期发展的量子加速成像（QAI）来应对 SDA 挑战。我们主要关注三个 SDA 挑战：（1）最小先验假设（2）多目标问题（3）极端亮度比。我们还展示了在大气湍流存在下的光源估计和定位结果。QAI 在所有 SDA 挑战中显示出显著改善的目标位置、亮度和数量估计。具体而言，我们证明了在极端场景（如亮度比为 1000 倍的恒星）中，光源检测性能比高度优优直接成像高 2.5 倍。通过超过 10,000 次模拟，我们验证了该方法相比传统最先进直接成像具有更高的分辨率，为 SDA 的量子光学方法铺平了道路。
英文摘要：
The growth in space activity has increased the need for Space Domain Awareness (SDA) to ensure safe space operations. Imaging and detecting space targets is, however, challenging due to their dim appearance, small angular size/separation, dense distribution, and atmospheric turbulence. These challenges render space targets in ground-based imaging observations as point-like objects in the sub-Rayleigh regime, with extreme brightness contrast but a low photon budget. Here, we propose to use the recently developed quantum-accelerated imaging (QAI) for the SDA challenge. We mainly focus on three SDA challenges (1) minimal a priori assumptions (2) many-object problem (3) extreme brightness ratio. We also present results on source estimation and localization in the presence of atmospheric turbulence. QAI shows significantly improved estimation in position, brightness, and number of targets for all SDA challenges. In particular, we demonstrate up to 2.5 times better performance in source detection than highly optimized direct imaging in extreme scenarios like stars with a 1000 times brightness ratio. With over 10,000 simulations, we verify the increased resolution of our approach compared to conventional state-of-the-art direct imaging paving the way towards quantum optics approaches for SDA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
