For the first time to our knowledge, we apply the multiple signal classification (MUSIC) algorithm to signals obtained from a self-mixing flow sensor. We find that MUSIC accurately extracts the fluid velocity and exhibits a markedly better signal-to-noise ratio (SNR) than the commonly used fast Fourier transform (FFT) method. We compare the performance of the MUSIC and FFT methods for three decades of scatterer concentration and fluid velocities from 0.5 to 50 mm/s. MUSIC provided better linearity than the FFT and was able to accurately function over a wider range of algorithm parameters. MUSIC exhibited excellent linearity and SNR even at low scatterer concentration, at which the FFT's SNR decreased to impractical levels. This makes MUSIC a particularly attractive method for flow measurement systems with a low density of scatterers such as microfluidic and nanofluidic systems and blood flow in capillaries. (C) 2015 Optical Society of America
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机译：据我们所知，我们首次将多信号分类（MUSIC）算法应用于自混合流量传感器获得的信号。我们发现，MUSIC比通常使用的快速傅立叶变换（FFT）方法能够准确地提取流体速度，并表现出明显更好的信噪比（SNR）。我们比较了MUSIC和FFT方法在从0.5到50 mm / s的三十年散射体浓度和流体速度方面的性能。 MUSIC提供了比FFT更好的线性度，并且能够在更大范围的算法参数中准确发挥作用。即使在低散射体浓度下，MUSIC仍具有出色的线性度和SNR，在这种情况下，FFT的SNR降低到不切实际的水平。这使得MUSIC特别适用于具有低密度散射体（例如微流体和纳米流体系统）以及毛细管中血流的流量测量系统。 （C）2015年美国眼镜学会
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