Model order and frequency estimation of multiple 2-D complex sinusoids in additive white Gaussian noise are addressed. Frequency estimation follows the coarse-fine search strategy. Coarse estimates, obtained by locating maxima of the 2-D discrete Fourier transform, are refined in a two-stage procedure. In both stages, frequency refinement is based on three-point periodogram maximization. In order to provide accurate model order estimation (MOE) for a wide signal-to-noise ratio (SNR) range, our approach combines two metrics for sinusoid detection in the 2-D frequency domain, one for low and the other for high SNR values. The proposed frequency estimation method attains the Cramér-Rao lower bound and it outperforms parametric methods in terms of the estimation accuracy and numerical efficiency. Compared with information criterion-based methods, the proposed MOE approach is numerically more efficient, it does not require estimation of noise variance and hence does not suffer from overestimation at high SNR.

解决了加性高斯白噪声中多个二维复正弦波的模型阶数和频率估计。频率估算遵循粗细搜索策略。通过定位2-D离散傅里叶变换的最大值而获得的粗略估计在两阶段过程中得到了完善。在两个阶段中，频率细化都是基于三点周期图最大化。为了在宽信噪比（SNR）范围内提供准确的模型阶估计（MOE），我们的方法结合了二维频域中正弦波检测的两个指标，一个指标低，另一个指标高。价值观。提出的频率估计方法达到了Cramér-Rao下界，在估计精度和数值效率方面均优于参数方法。与基于信息标准的方法相比，