Identification of two dimensional complex sinusoids in white noise: a state-space frequency approach

This paper proposes a new frequency domain approach for identifying the parameters of two-dimensional complex sinusoids from a finite number of data, when the measurements are affected by additive and uncorrelated two-dimensional white noise. The new method extends in two dimensions a frequency identification procedure of complex sinusoids, originally developed for the one-dimensional case. The properties of the proposed method are analyzed by means of Monte Carlo simulations and its features are compared with those of other estimation algorithms. In particular the practical advantage of the method is 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.