ROBUST 2-DIMENSIONAL FREQUENCY ESTIMATION BY USING HIGHER-ORDER STATISTICS

This paper describes a new high resolution algorithm for the two-dimen sional (2-D) frequency estimation problem, which, in particular, is no ise insensitive in view of the fact that in many practical application s the contaminated noise may not be white noise. For this purpose, the approach is set in the context of higher-order statistics (HOS), whic h has demonstrated to be an effective approach under a colored noise e nvironment. The algorithm begins with the consideration of the fourth- order moments of the available 2-D data. Two auxiliary matrices, const ituted by a novel stacking of the diagonal slice of the computed fourt h-order moments, are then introduced and through which the two frequen cy components can be precisely determined, respectively, via matrix fa ctorizations along with the subspace rotational invariance (SRI) techn ique. Simulation results are also provided to verify the proposed algo rithm.