A covariation-based algorithm for estimating 2-D DOA in the presence of impulsive nise

Constructs a particular array geometric configuration, and defines the cross-covariation matrix of the sub-array sensor outputs. Final, a subspace-based 2-D bearing estimation algorithm using fractional lower order statistics is proposed in the presence of impulsive noise which can be modeled as a complex symmetric alpha-stable (SaS) process. The method extends signal models and application situation of 2-D direction finding algorithm by exploiting the infinite pth-moments for p > a and finite fractional pth-order moments only for 0 < p < a for SaS processes. The algorithm is robust against additive SaS noise, which remedies the lack of the traditional subspace-based techniques employing both second-order or higher order moments cannot be applied in impulsive noise environments. The simulation results show the feasibility and effectiveness of the algorithm.