Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 70, Issue 2, Pages 1952-1956Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3051266
Keywords
Estimation; Radar; Covariance matrices; Complexity theory; Sensors; Multiple signal classification; Massive MIMO; Automotive radars; low-rank approximation; massive MIMO; mm-wave; randomized MUSIC
Categories
Funding
- Natural Science Foundation of China (NSFC) [U1805262]
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The study introduces a fast randomized-MUSIC (R-MUSIC) algorithm that utilizes random matrix sketching to estimate signal subspace, reducing computational complexity while maintaining high accuracy. This method resolves the contradiction between complexity and accuracy in MIMO radar signal processing, showing potential for real-time super-resolution automotive sensing.
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.
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