Resolving power of signal subspace methods for finite data lengths

The signal subspace algorithm, based on functions of the eigenvectors and eigenvalues of a data covariance matrix, is often used as a "high resolution" parameter estimator. In this paper, the resolving power of a signal subspace method is studied. By employing the statistical distributions of the eigenvectors of a sample covariance matrix, a measure of the expected resolving power of the MUSIC source direction estimator is obtained. The analysis shows that the ability of the MUSIC algorithm to resolve two closely spaced sources incident on an array of sensors is strongly linked to the observation time, the signal to noise ratio, and the separation between the sources.