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.