60GHz移动成像雷达

Yibo Zhu, Yanzi Zhu, Zengbin Zhang, Ben Y. Zhao, Haitao Zheng
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引用次数: 28

摘要

移动计算正在经历一场重大变革。传统的移动网络以用户及其移动为中心,而新的网络通常以自主移动代理为中心。其中包括执行军事任务的半自动无人机,在家中搜寻灰尘的真空机器人,送我们去上班的智能汽车,以及在灾难中发现和救援受害者的第一反应机器人。限制这些自主设备的一个关键挑战是缺乏精确的传感系统,例如,捕捉附近物体的位置、形状和表面材料的移动成像系统。这些设备通常需要很高的精度,并且在严格的限制下运行:在低光条件下或以中等速度移动。这些限制极大地减少了可能的解决方案,消除了依赖可见光或专用硬件的传统成像系统。在本文中,我们展示了我们利用60GHz无线波束反射设计和评估数字成像雷达系统的早期成果。利用用户移动性对大孔径虚拟天线阵列进行仿真,构建了大孔径、高精度的虚拟天线。我们详细描述了我们的设计,包括机制的目标检测,目标成像,和控制精度。我们在一个真实的60GHz测试平台上的实验表明,我们可以在用户移动半米的情况下实现高精度(~1厘米)成像,并且增加了使用损耗曲线推断被检测物体表面材料的潜力。
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60GHz Mobile Imaging Radar
Mobile computing is undergoing a significant shift. Where traditional mobile networks revolved around users and their movements, new networks often center around autonomous mobile agents. These include semi-autonomous drones on military missions, vacuum robots search for dirt at home, intelligent cars that deliver us to work, and first responder robots that find and rescue victims in disasters. A critical challenge limiting these autonomous devices is the lack of accurate sensing systems, e.g. a mobile imaging system that captures the position, shape and surface material of nearby objects. These devices often require high levels of accuracy, and operate under tight constraints: in low-light conditions or moving at moderate speeds. These constraints dramatically reduce the set of possible solutions, eliminating traditional imaging systems that rely on visible light or specialized hardware. In this paper, we present early results in our efforts to design and evaluate a digital imaging radar system using reflections from 60GHz wireless beams. By using user mobility to emulate a virtual antenna array with large aperture, we build virtual antennas with large aperture and high precision. We describe details of our design, including mechanisms for object detection, object imaging, and controlling precision. Our experiments on a real 60GHz testbed show that we can achieve high precision (~1 cm) imaging with as little user movement as half a meter, as well as added potential for using loss profiles to infer the surface material on detected objects.
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