用数据矩阵奇异值分解实现时间欠采样信号频率无模糊估计

提出一种在欠Ｎｙｑｕｉｓｔ采样下可实现高宽频带内信号频率无模糊估计的算法，该算法通过构造一种特殊的数据矩阵，对数据矩阵奇异值分解，利用有限的采样数据构造出更为精确的噪声子空间，利用信号子空间与噪声子空间的正交性，精确地提取多个空间信号的频率信息。该法比基于协方差矩阵特征分解的算法具有更好的分辨性能。仿真结果验证了该算法的有效性。

Frequency Estimation with Sub Nyquist Sampling Using the Singular Value Decomposition of a Special Data Matrix

A method is proposed to estimate frequency with sub Nyquist sampling using the singular value decomposition (SVD) of a special data matrix.It can get more precise noise space in infinite sample date. By using the orthogonal properties of the noise space and the signal space,the frequency information of multiple spatial arrival is got exactly. The property of estimation is better than that of the method based on the eigen structure decomposition of a covariance matrix. The results of computer simulation illustrate that the new method is quite effective.