It is well known that the performance of direction-of-arrival estimation and beamforming algorithms degrades in the presence of correlated signals. Since techniques like spatial and weighted smoothing, which were developed for decorrelating the signals, suffer from reduced effective aperture, several authors have recently proposed redundancy averaging as an alternative spatial averaging method. Considering a two-source signal model and the asymptotic case, the authors show analytically that this technique results in an eigenstructure which is inconsistent with that of the underlying signal model. In particular, the resulting signal subspace fills up the whole M-dimensional space, where M is the array size, and the resulting covariance matrix is not guaranteed to be nonnegative definite. It is shown that complete decorrelation is not possible with this method even if the array size is infinitely large.
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