Until recently most high-resolution autoregressive spectral analysis techniques have been applied to analysis of either single-channel waveforms or multichannel vector processes. However, the use of data prediction autoregressive spectral analysis permits the application of some high-resolution single-channel methods to high-resolution spectral analysis of data fields in two or more dimensions. The technique consists of extrapolating observed data beyond the observation window by means of an autoregressive data-generation model. High-resolution spectral analyses are then obtained by conventional discrete Fourier transforms (DFTs) of the extrapolated data.