Bearing discrimination methods for MUSIC, ESPRIT and moving window

The problem of discriminating accurate bearing estimates from those estimates likely to be corrupted by gross error is considered. Bearing discrimination functions are presented for use in association with the MUSIC, ESPRIT, and the moving window eigen-based DF algorithms. In the case of the MUSIC algorithm, the discriminate function is based on the statistics of the noise background. The reliability of the bearing was determined by how robust the spectral peak was, compared to the noise background. The bearing discrimination methods developed for the ESPRlT and moving window algorithms are based on a measure of wavefront planarity. Relative performance is evaluated for signal conditions routinely encountered in the high-frequency environment