@TechReport{	  it:2017-020,
  author	= {Umberto Soverini and Torsten S{\"o}derstr{\"o}m},
  title		= {2D-Frequency Domain Identification of Complex Sinusoids in
		  the Presence of Additive Noise},
  institution	= {Department of Information Technology, Uppsala University},
  department	= {Division of Systems and Control},
  year		= {2017},
  number	= {2017-020},
  month		= oct,
  note		= {Updated by Technical Report 2018-006, April 2018. See
		  \url{http://www.it.uu.se/research/publications/reports/2018-006}.}
		  ,
  abstract	= {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. As a
		  major feature, it enables the estimation to be frequency
		  selective. 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.}
}