Min-norm interpretations and consistency of MUSIC, MODE and ML

The multiple signal characterization (MUSIC) approach, its generalization to correlated signals known as the method of direction estimation (MODE), and the deterministic maximum likelihood (ML) approach for bearing estimation in array processing are shown to be signal subspace fitting approaches in...

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Bibliographic Details
Published in:IEEE transactions on signal processing 1995-12, Vol.43 (12), p.2937-2942
Main Authors: Ephraim, Y., Merhav, N., Van Trees, H.L.
Format: Article
Language:English
Subjects:
Applied sciences
Covariance matrix
Direction of arrival estimation
Exact sciences and technology
Life estimation
Maximum likelihood estimation
Multiple signal classification
Parameter estimation
Radiolocalization and radionavigation
Sensor arrays
Sensor phenomena and characterization
Signal processing
Telecommunications
Telecommunications and information theory
White noise
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Summary:The multiple signal characterization (MUSIC) approach, its generalization to correlated signals known as the method of direction estimation (MODE), and the deterministic maximum likelihood (ML) approach for bearing estimation in array processing are shown to be signal subspace fitting approaches in a minimum norm sense. MODE, for example, is shown to be an approach in which the array manifold is linearly estimated from principal empirical eigenvectors in a minimum weighted Frobenius norm sense. Using the min-norm interpretations, a unified proof for strong consistency of the three approaches is provided for stationary and ergodic signals.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.476437