Eigenfilter methods for 2D spectral estimation

The paper presents two methods for the determination of 2D eigenfilter spectra, both of which can be viewed as a 2D extension of the conventional Pisarenko technique. The first approach taken is to formulate the problem as the design of a 2D moving average filter whose output energy must be minimised subject to a specified constraint. A second 2D eigenspectra technique can be developed by modelling 2D sinusoids in white noise. In both cases the underlying process spectra is determined from an eigenvector of an autocorrelation matrix. It is shown that when the second technique is used the autocorrelation matrix required can always be of minimal size.