An efficient subspace algorithm for 2-D harmonic retrieval

This paper addresses the problem of estimating the frequency content of a two-dimensional object, e.g. an image or a set of multi-sensor snapshots, stored in a matrix. The basic assumption is that the data matrix consists of a sum of 2-D complex sinusoids. Our algebraically coupled matrix pencils (ACMP) algorithm splits the 2-D problem into two related 1-D estimation problems. In each direction the frequencies are estimated using a computationally efficient ESPRIT-like subspace algorithm. A further increase in efficiency is due to the algebraic pairing of the horizontal and vertical estimates