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arXiv 2110.03218eess.SPcs.CVcs.LG
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MIMO雷达成像系统设计与重构的联合优化

Joint optimization of system design and reconstruction in MIMO radar imaging

Technion -- Israel Institute of Technology(以色列理工学院)

Tomer Weiss, Nissim Peretz, Sanketh Vedula, Arie Feuer, Alex Bronstein

摘要:

多输入多输出(MIMO)雷达是主要的深度感测模态之一。然而,多个接收信道的使用导致相对高的成本,并且阻止MIMO在诸如汽车工业的许多领域中的渗透。在过去的几年里,很少有研究集中在设计减少测量方案和图像重建方案的MIMO雷达,但这些问题已经被单独解决到目前为止。另一方面,最近的光学计算成像的工作已经证明了越来越多的成功的同时学习为基础的设计的采集和重建方案,表现出显着改善重建质量。受这些成功的启发,在这项工作中,我们建议学习MIMO采集参数的形式接收(Rx)天线单元的位置与图像神经网络为基础的重建。为此,我们提出了一种算法,用于以可区分的方式训练组合的采集-重建管道端到端。我们证明了使用我们学到的采集参数的意义,有和没有神经网络重建。

英文摘要:

Multiple-input multiple-output (MIMO) radar is one of the leading depth sensing modalities. However, the usage of multiple receive channels lead to relative high costs and prevent the penetration of MIMOs in many areas such as the automotive industry. Over the last years, few studies concentrated on designing reduced measurement schemes and image reconstruction schemes for MIMO radars, however these problems have been so far addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of simultaneous learning-based design of the acquisition and reconstruction schemes, manifesting significant improvement in the reconstruction quality. Inspired by these successes, in this work, we propose to learn MIMO acquisition parameters in the form of receive (Rx) antenna elements locations jointly with an image neural-network based reconstruction. To this end, we propose an algorithm for training the combined acquisition-reconstruction pipeline end-to-end in a differentiable way. We demonstrate the significance of using our learned acquisition parameters with and without the neural-network reconstruction.

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。

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