Millimeter-wave radar, in marriage with virtual massive multiple-input multiple-output (MIMO) system, provides great promises to super-resolution environment sensing and target detection in unmanned ground/aerial vehicle (UGA/UAV). However, the existing target detection algorithms suffer from either  low resolution/accuracy or  high time complexity. In this work , we propose two efficient approximate algorithms to significantly accelerate the high-resolution subspace algorithm. In particular, our fast MUST algorithms speed up the expensive signal subspace identification step using the randomized low-rank approximations. We also establish theoretical bounds to guarantee the quality of approximation. Specifically, we show that, in the case of high signal-to-noise ratio, the pseudo-spectrum produced by our algorithms are very close to the exact MUSIC. Our theoretical analysis is original, which does not simply follow from any existing work. Our comprehensive empirical study shows that our algorithms are tremendously faster than MUSIC, while their target detection accuracies in massive MIMO radar are almost as good as MUSIC. This work enables super-resolution automotive sensing in real time and thus has great practical value in emerging automotive applications.
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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Millimeter-wave radar, in marriage with virtual massive
Massive
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system,
radar, assisted by millimeter-wave band virtual MIMO techniques,
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super-resolution environment
the high-resolution automotive
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vehicle
vehicles
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However, the existing target detection algorithms
As one long-standing challenging problem, however existing subspace methods may
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the
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the
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work
study
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efficient approximate algorithms to significantly accelerate
computational efficient methods to accomplish
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subspace algorithm. In particular, our fast MUST algorithms
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
R
expensive signal subspace identification step using the randomized low-rank approximations. We also establish theoretical bounds to guarantee the quality of approximation. Specifically, we show that,
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,
R
produced by our algorithms are very close
acquired by our fast-MUSIC is highly precise, when compared
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Our theoretical analysis is original, which does not simply follow from any existing work. Our comprehensive empirical study shows that our algorithms
Comprehensive numerical study demonstrates that our new methods
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their target detection accuracies in massive MIMO radar
the AoA estimation accuracy
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This work enables super-resolution automotive sensing in real time and thus has great practical value in emerging
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