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Determining Autocorrelation Matrix Size and Sampling Frequency for MUSIC Algorithm | IEEE Journals & Magazine | IEEE Xplore

Determining Autocorrelation Matrix Size and Sampling Frequency for MUSIC Algorithm


Abstract:

Detectability of closely spaced sinusoids in a noisy signal using MUltiple SIgnal Classifier (MUSIC) depends to a great extent on the sampling frequency (Fs) and the size...Show More

Abstract:

Detectability of closely spaced sinusoids in a noisy signal using MUltiple SIgnal Classifier (MUSIC) depends to a great extent on the sampling frequency (Fs) and the size of the autocorrelation matrix (N). Improper choice of any of these may result in increased computational burden and/or unresolved frequency components. This paper presents an analytical approach to determine expressions of lobe width using Fs and N at lobe base (Δfb) and half of the lobe height (Δfh). The required values of Fs and N can be derived from the expression of Δfb for distortion-less lobe heights of two closely spaced sinusoids. A tighter bound can be found using the expression of only Δfh to resolve two distinct peaks. Probability of resolution using reciprocal of MUSIC peaks is determined for various N and it's limit for full resolvability was verified with the derived analytical expressions.
Published in: IEEE Signal Processing Letters ( Volume: 22, Issue: 8, August 2015)
Page(s): 1016 - 1020
Date of Publication: 31 October 2014

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