Direction finding in non-Gaussian impulsive noise environments

The class of alpha-stable distributions is better for modeling impulsive noise than Gaussian distribution in array signal processing. This paper briefly introduces the statistical characteristics of stable distribution and its fractional lower-order statistics, covariation and fractional-order correlation, and proposes a new minimum norm method (FOC–MinNorm) of direction finding based on the fractional-order correlation and subspace technique under alpha-stable noise conditions. We analyze the performances of the FOC–MinNorm, including the accuracy in the estimation, the capability and probability of resolution, and the pseudo peaks of the FOC–MinNorm method. The analysis is based on the assumption that the additive noise can be modeled as a complex alpha-stable process. Simulation and analysis show that the proposed method is robust in a wide range of characteristic exponent values of stable distribution. Its resolution capability and probability of resolution are better than those of the conventional second-order statistics based MinNorm algorithm and covariation based ROC–MUSIC method, furthermore, fractional-order correlation is more suitable than covariation in practical applications.