2D-frequency domain identification of complex sinusoids in the presence of additive noise

This paper describes a new approach for identifying the parameters of two-dimensional complex sinusoids from a finite number of measurements, in presence of additive and uncorrelated two-dimensional white noise. The proposed approach is based on using frequency domain data. The new method extends to the two-dimensional (2D) case some recent results obtained with reference to the frequency ESPRIT algorithm. The properties of the proposed method are analyzed by means of Monte Carlo simulations and its features are compared with those of a classical time domain estimation algorithm. The practical advantages of the method are highlighted. In fact the novel approach can operate just on a specified sub-area of the 2D spectrum. This area-selective feature allows a drastic reduction of the computational complexity, which is usually very high when standard time domain methods are used.