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

机译：存在加性噪声时复杂正弦波的二维频域识别

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摘要

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.
机译：本文介绍了一种新方法，该方法可在有限的测量次数下，在存在加性和不相关的二维白噪声的情况下，识别二维复杂正弦波的参数。所提出的方法基于使用频域数据。新方法扩展到二维（2D）情况，参考频率ESPRIT算法获得了一些最新结果。通过蒙特卡洛仿真分析了该方法的性质，并与经典时域估计算法进行了比较。突出了该方法的实际优势。实际上，该新颖方法可以仅在2D频谱的指定子区域上运行。这种区域选择功能可大大降低计算复杂度，当使用标准时域方法时，通常这是非常高的。

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