It is well known that the performance of direction-of-arrival (DOA) estimation and beamforming algorithms degrades in the presence of correlated signals. Since the 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. In this correspondence, we analyze this method for the asymptotic case and show that the eigenstructure of the resulting matrix, after redundancy averaging, is inconsistent with that of the underlying signal model, thereby leading to biased DOA estimates. Further, perfect decorrelation of the signals is not guaranteed even if the array size is made infinitely large.

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