  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.
