A novel MUSIC algorithm based on cyclic correntropy in impulsive noise

Since the DOA estimation based on second-order cyclic statistics degrades seriously in an α-stable distribution noise environment, and the fractional lower-order statistics based methods depend on apriori knowledge of non-Gaussiannoise. In this paper, by exploiting both the cyclic correlation and correntropy, a novel DOA algorithms based on cyclic correntropy is proposed to deal with cyclostationary signals under impulsive noise environment based on kernel methods. The algorithm allows to select desired signals and to ignore interferences in communication system which suppresses the interferences of the same frequency band. Simulation results strongly verify the effectiveness of this method.