Improved cyclic and conjugate cyclic MUSIC

By exploiting cyclostationarity that is inherent in many communications signals, cyclic MUSIC has been shown to be able to perform signal selective direction of arrival (DOA) estimation under colored noise and clutter. However, the DOA estimation of cyclic MUSIC is actually biased. We show in this paper that the bias of DOA estimation can be substantially reduced and the performance can be improved by properly choosing the frequency for evaluating the steering vector. We also propose another algorithm that exploits cyclic conjugate correlation to further improve the performance of DOA estimation. Simulation results show the effectiveness of both approaches.