Having a hard time wrapping my head around autocorrelation matrix as it applies to a spectral estimation problem like MUSIC or ESPRIT. If the signal vector contains a summation of sinusoids in noise, how does building the autocorrelation matrix help decipher this information? Seems like a bit of magic that the toeplitz matrix of the autocorrelation sequence can help break out the signal subspace and the noise subspace.


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