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- W1993770090 abstract "This paper studies the application of MUtiple Signal Classification (MUSIC) algorithm on Multiple Measurement Vector (MMV) problem for the purpose of frequency parameter estimation while s true frequencies are located in the continuum of a bounded domain and sensors are randomly selected from a Uniform Linear Array (ULA). The MUSIC algorithm amounts to identifying a noise subspace from measurements, forming a noise-space correlation function and searching the s smallest local minima of the noise-space correlation function. Under the assumption that the true frequencies are separated by at least one Rayleigh Length (RL), we show that with high probability the noise-space correlation function is stably perturbed by noise if the number of sensors n ~ O(s) up to a logarithmic factor by means of a compressive version of discrete Ingham inequalities. As the theory implies, our numerical experiments demonstrate that the reconstruction error of MUSIC with n random sensors makes little difference once n is above a point of transition." @default.
- W1993770090 created "2016-06-24" @default.
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- W1993770090 date "2014-12-01" @default.
- W1993770090 modified "2023-10-16" @default.
- W1993770090 title "MUSIC for joint frequency estimation: Stability with compressive measurements" @default.
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- W1993770090 doi "https://doi.org/10.1109/globalsip.2014.7032150" @default.
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