The subspace-based techniques are widely utilized to estimate the parameters of sums of complex sinusoids corrupted by noise, and the zoom ESPRIT algorithm utilizes the zoom technique to apply the ESPRIT to a narrow frequency band to improve the accuracy of frequency estimation. However, the Gaussian noise becomes non-Gaussian in the zoomed baseband after being filtered by a low-pass filter, and thus has an unknown covariance matrix. However, most exiting algorithms for model order estimation performs poorly for the case of colored noise with unknown covariance matrix. In order to accurately estimate the dimension of the signal subspace for the zoom ESPRIT algorithm, this paper proposes a novel strategy to estimate the number of signals for the case of colored noise with unknown covariance matrix. The proposed strategy is based on the analysis of the behavior of information theoretic criteria utilized in model order selection. Firstly, a first criterion is defined as the ratio of the current eigenvalue and the mean of the next ones, and its properties is analyzed with respect to the over-modeling and under-modeling. Secondly, a novel second criterion is designed as the ratio of the current value and the next value of the first criterion, and its properties is also analyzed with respect to the over-modeling and under-modeling. Then, a novel signal number estimation method is proposed by combining the second criterion with the first criterion to check whether the eigenvalue being tested is arising from a signal or from noise. The resulted signal number estimation method is called as the double criterion-based estimator as it utilizes two criteria to separate the signal eigenvalues from the noise eigenvalues. Finally, simulation results are presented to illustrate the performance of the proposed double criterion-based estimator and compare it with the existing methods.
翻译:以子空间为基础的技术被广泛用来估计因噪音而腐蚀的复合鼻固醇的数值,而缩放ESPRIT 算法则则则利用缩放技术将ESPRIT应用到一个窄频波中,以提高频率估计的准确性。然而,高西噪音在通过低通过滤器过滤后,在缩放基带中成为非Gausian。因此,它有一个未知的共变差矩阵。然而,大多数模型订单估算的离位算法对于彩色噪音和未知的共变异矩阵的情况来说,表现不佳。为了准确估计缩放ESPRIT 算法的信号子空间的尺寸,本文件采用缩放技术将ESPRIT 应用到一个窄频频带,以提高频率估计的准确性能。然而,高西噪音的噪音在缩放基带中成为非Gausian,因为通过低通道过滤过滤器过滤器过滤,因此第一个标准被定义为当前egenvalue和下一个数值的基数,其属性被分析为第二个基数的基数,而第二个基数则从基数,第二个基价和第一个基底信号的基值正在上升,第二个基比使用当前标准,第二个基比,第二个标准正在使用新标准,第二个标准正在根据新标准设计,第二个标准,第二个标准,第二个标准是计算。第二个标准是根据新标准,第二个标准,第二个标准,第二个标准,第二个标准是计算,第二个标准,第二个标准,第二个标准是基价值,第二个标准是基价值,第二个标准,第二个标准,第二个基价值与第二个基值与当前基价值,第二个基值,第二个基值,第二个基值,第二个基值,第二个基值,第二个基值,第二个基值与当前基值与当前基值与当前基值,一个基值与当前基值,一个基值与当前基值,一个基值,一个基值与当前基值比,一个基值,一个基值与当前基值,一个基值与后基值,一个基值,一个基值,一个基值,一个基值,一个基值,一个基值,一个基值与第二个基值,一个基值,一个基值,一个基值,一个基值与后基值,一个基值,一个基值,一个基值,一个基值,一个基值与