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We propose an algebraic approach for N-D frequency estimation using the eigenvectors of a matrix pencil constructed from the signal subspace of the data sample covariance matrix. Unlike existing eigenvalue-based methods, the proposed algorithm achieves automatic frequency pairing without using joint diagonalization; thus, the computational complexity is reduced. The proposed algorithm remains operational...
Recently much progress has been made to improve the identifiability of frequency estimation from one snapshot of multidimensional frequency data mixture. However, in the case of multiple snapshots (or multiple trials of experiments), there are few identifiability results available. With multiple data snapshots, most existing algebraic approaches estimate frequencies from the sample covariance matrix...
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