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.

翻译：多重投入多重输出(MIIMO)雷达是主要的深度测深方法之一,但是,使用多种接收渠道导致相对高的成本,防止MOIMO渗透到汽车工业等许多领域,在过去几年中,很少有研究集中于设计减少的测量计划和MIMO雷达图像重建计划,但这些问题迄今已经分开处理。另一方面,光学计算成像的近期工程显示,以学习为基础同时设计购置和重建计划,这显示出重建质量的显著改善。在这种成功的启发下,我们提议以接收(Rx)天线要素地点的形式学习MOIMO的获取参数,同时进行基于图像神经网络的重建。为此,我们提出一种算法,以不同的方式培训合并购置-重建管道的终端到终端。我们展示了利用我们所学的获取参数和不进行神经网络重建的重要性。