High-resolution signal parameter estimation is a problem of significance in many signal processing applications. Such applications indude direction-of-arrival estimation, system identification, and time series analysis. A novel approach to the general problem of signal parameter estimation is described. Although discussed in the context of directionof-
arrival estimation, ESPRIT can be applied to a wide variety of problems. It exploits an underlying rotational invariance among signal subspaces
induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as Burg's maximum entropy method, Capon's maximum likelihood method, and Schmidt's multiple signal classification.