Fast-MUSIC for Automotive Massive-MIMO Radar
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Massive multiple-input multiple-output (MIMO) radar, assisted by millimeter-wave band virtual MIMO techniques, provides great promises to the high-resolution automotive sensing and target detection in unmanned ground/aerial vehicles (UGA/UAV). As one long-standing challenging problem, however existing subspace methods may suffer from either the low resolution/accuracy or the high time complexity. In this study, we propose two computational efficient methods to accomplish the high-resolution estimation of angle of arrival (AoA) information. By leveraging randomized low-rank approximation, our fast-MUSIC approaches, relying on random sampling and projection techniques, would speed up the subspace computation by orders of magnitude. At the same time, we establish the theoretical bounds of our proposed approaches, which ensure the accuracy of approximated pseudo-spectrum. As shown, in the case of high signal-to-noise ratio, the pseudo-spectrum acquired by our fast-MUSIC is highly precise, when compared to the exact MUSIC. Comprehensive numerical study demonstrates that our new methods are tremendously faster than MUSIC, while the AoA estimation accuracy are almost as good as MUSIC. As such, our fast-MUSIC enables the high-resolution yet real-time sensing with massive MIMO radar, which has great potential in the emerging mobile computing and automotive applications.
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