Massive multiple-input multiple-output (MIMO) radar, enabled by millimeter-wave virtual MIMO techniques, provides great promises to the high-resolution automotive sensing and target detection in unmanned ground/aerial vehicles (UGA/UAV). As a long-established problem, however, existing subspace methods suffer from either high complexity or low accuracy. In this work, we propose two efficient methods, to accomplish fast subspace computation and accurate angle of arrival (AoA) acquisition. By leveraging randomized low-rank approximation, our fast multiple signal classification (MUSIC) methods, relying on random sampling and projection techniques, substantially accelerate the subspace estimation by orders of magnitude. Moreover, we establish the theoretical bounds of our proposed methods, which ensure the accuracy of the approximated pseudo-spectrum. As demonstrated, the pseudo-spectrum acquired by our fast-MUSIC would be highly precise; and the estimated AoA is almost as accurate as standard MUSIC. In contrast, our new methods are tremendously faster than standard MUSIC. Thus, our fast-MUSIC enables the high-resolution real-time environmental sensing with massive MIMO radars, which has great potential in the emerging unmanned systems.

翻译：由毫米波虚拟MIMO技术促成的大规模多投入多输出雷达,为无人驾驶地面/空中飞行器(UGA/UAV)的高分辨率汽车感测和目标探测提供了巨大的希望。然而,作为一个长期存在的问题,现有的子空间方法要么是高度复杂,要么是精确度低的问题。在这项工作中,我们提出了两种有效的方法,以完成快速子空间计算和准确抵达角度(AoA)的获取。通过利用随机的低级别近似,我们快速多信号分类(MUSIC)方法,依靠随机抽样和投射技术,大大加快了子空间的量级估计。此外,我们确定了我们拟议方法的理论界限,确保了近似伪光谱的准确性。正如所显示的那样,我们快速MUSIC的假光谱将非常精确;估计AoA的精确度与标准MUSICE几乎一样。相比之下,我们的新方法比标准MISIC快得多。因此,我们的快速多信号分类方法使得高分辨率实时环境测测算系统能够对正在形成的大型的无人驾驶式雷达系统进行巨大的实时环境探测。