This paper describes a new approach for identifying the parameters of twoâdimensional complex sinusoids from a finite number of measurements, in presence of additive and uncorrelated twoâdimensional white noise. The proposed approach is based on using frequency domain data. As a major feature, it enables the estimation to be frequency selective. The new method extends to the twoâdimensional (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 subâarea of the 2D spectrum. This areaâselective feature allows a drastic reduction of the computational complexity, which is usually very high when standard time domain methods are used.
Note: Updated by Technical Report 2018-006, April 2018. See http://www.it.uu.se/research/publications/reports/2018-006.
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