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Adaptive Near-Field Imaging with Robotic Arrays

机译:具有机器人阵列的自适应近场成像

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In this paper, we are interested in phased-array imaging of an unknown area using narrowband RF signals and arrays synthesized by an unmanned vehicle. Typical phased array imaging approaches use fixed or pre-determined array configurations for imaging, which are not usually informative for the whole area. In this paper, we then propose an iterative adaptive imaging approach where we identify the uncertain regions in an initial image that need to be sensed better, find the optimal array location and orientation for such a sensing task and finally combine the new image with the initial image to obtain a better overall imaging quality. Such an approach is easily enabled by using robots to synthesize arrays. We validate our proposed approach with numerical simulations and show that an adaptive approach improves the imaging quality as compared to imaging with fixed arrays.
机译:在本文中,我们对使用窄带RF信号和由无人驾驶车辆合成的阵列进行分阶段阵列成像。典型的相控阵列成像方法使用固定或预定的阵列配置进行成像,这通常不会提供整个区域的信息。在本文中,我们提出了一种迭代的自适应成像方法,在那里我们在需要更好地识别需要感测的初始图像中识别不确定的区域,找到这种感测任务的最佳阵列位置和方向,最终将新图像与初始相结合图像获得更好的整体成像质量。通过使用机器人来合成阵列,易于启用这种方法。我们通过数值模拟验证了我们提出的方法,并表明与固定阵列的成像相比,自适应方法提高了成像质量。

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