Eigenstructure variability of the multiple-source multiple-sensor covariance matrix with contaminated Gaussian data

Several methods of current interest for counting and locating signal sources using data from a passive array depend on the accuracy of estimating the eigenstructure of the covariance matrix of the array's data vectors. When errors in the measured data vectors are Gaussian conventional covariance estimation is optimal, but robust procedure are required for data with nonGaussian additive contamination. Two different robust covariance estimators are compared by simulation with the conventional one for different degrees of contamination. Even in relatively good signal-to-noise ratios, however, closeness of signal sources in the temporal and spatial frequency domains can cause inaccurate signal-related eigenvalue and eigenvector estimates. The degree of adversity for these problems is also shown by simulation.<
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