Analysis of the performance and sensitivity of eigendecomposition-based detectors

A new framework is presented for the analysis of the performance of detection methods, such as AIC and MDL, which are based on the eigenvalues of the sample covariance matrix. It is shown that theoretical analysis of the probabilities of overestimation and underestimation can be much more conveniently carried out via a proposed, particularly simple, sequence of statistics. Also, the breakdown of these detection methods in the presence of model nonidealities is explored by theory, simulations, and experimentation with real array data. For example, theoretical arguments are given to demonstrate the high degree of sensitivity of the detectors to unknown deviations of the noise from whiteness.<
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