Cyclic MUSIC algorithms for signal-selective direction estimation

Signal-selective direction-finding algorithms that overcome many of the limitations of existing techniques are presented. The algorithms automatically classify signals as desired or undesired on the basis of their known spectral correlation properties and estimate only the desired signals´ directions of arrival. The signal-selective nature of the techniques eliminates the need for knowledge of the characteristics of the noise or interference in the environment and makes it possible to resolve a number of desired signals not exceeding the number of sensors in the presence of arbitrary noise and a virtually unlimited number of unknown interferers. For example, the interferers can exhibit an arbitrarily high degree of correlation among themselves and can arrive from directions arbitrarily close to those of the desired signals