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The procedure of maximum‐entropy spectral analysis (MESA), used in the processing of time series data, also applies to wavenumber (bearing) analysis of signals received from a spatially distributed linear array of sensors. The method is precisely the use of autoregressive spectral analysis in the space dimension rather than in time. There are also close links to the predictive deconvolution method used in geophysical work, and to the process of constructing noise‐whitening filters in communication theory, as well as to least‐squares model building. In this note, we review the maximum‐entropy procedure pointing out all these links. The specific algorithm appropriate to a uniformly spaced line array of sensors is given, as well as one possible algorithm for use in the case of nonuniform sensor spacing.

Adams correctly points out a blunder of mine, in that the spatial correlations [Formula: see text] in my equation (75) should be identified with the correlations [Formula: see text] in equation (32), and not with the data samples [Formula: see text] in equation (11). In effect, the averaging in equation (67) replaces the averaging in equation (12). In the case of nonuniform array spacing, the necessary interpolation can still be done in [Formula: see text], as I suggest, but one might also interpolate directly in the Fourier coefficients [Formula: see text], or even in the data [Formula: see text]. These latter two possibilities would require more computation, however. It is in the details of the data averaging and interpolation that experimentation must show the way.

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