Subspace methods are essential to high-resolution environment sensing in the emerging unmanned systems, if further combined with the millimeter-wave (mm-Wave) massive multi-input multi-output (MIMO) technique. The estimation of signal/noise subspace, as one critical step, is yet computationally complex and presents a particular challenge when developing high-resolution yet low-complexity automotive radars. In this work, we develop a fast randomized-MUSIC (R-MUSIC) algorithm, which exploits the random matrix sketching to estimate the signal subspace via approximated computation. Our new approach substantially reduces the time complexity in acquiring a high-quality signal subspace. Moreover, the accuracy of R-MUSIC suffers no degradation unlike others low-complexity counterparts, i.e. the high-resolution angle of arrival (AoA) estimation is attained. Numerical simulations are provided to validate the performance of our R-MUSIC method. As shown, it resolves the long-standing contradiction in complexity and accuracy of MIMO radar signal processing, which hence have great potentials in real-time super-resolution automotive sensing.

翻译：子空间方法对于新兴无人系统的高分辨率环境感测至关重要,如果与毫米波(mm-Wave)大规模多投入多输出(MIMO)技术进一步结合的话。作为一个关键步骤,对信号/噪音子空间的估算在计算上仍然十分复杂,在开发高分辨率但低复杂性的汽车雷达时构成特别的挑战。在这项工作中,我们开发了快速随机MUSIC(R-MUSIC)算法,利用随机矩阵草图来估计信号子空间的近似计算。我们的新方法大大降低了获得高质量信号子空间的时间复杂性。此外,R-MUSIC的准确性与其他低兼容性对应方相比没有降低,即达到了高分辨率的抵达角度。提供了数值模拟,以验证我们R-MUSIC方法的性能。正如所显示的那样,它解决了MS雷达处理在复杂性和准确性方面长期存在的矛盾,因此在实时超分辨率的汽车遥感中具有巨大的潜力。