为了解决信息论准则估计信源数在有色噪声背景下失效以及盖氏圆方法在低信噪比下性能下降的问题，提出了利用延时相关预处理的信源数估计方法. 首先构造接收数据之间的延时相关矩阵，抑制噪声影响. 再将特征向量按划分规则分组，得到两类不同的特征空间，其中一类只含噪声特征矢量，另一类同时含有噪声和信号的特征矢量. 最后将延时相关矩阵投影到特征空间，通过对投影数据进行处理实现信源数估计. 仿真实验和实测结果表明了该算法的正确性和有效性.

Source number estimation based on information theoretic criteria is ineffective in colored noise field, and the performance of the Gerschgorin radii algorithm may decline under low signal-to-noise ratio conditions. To solve these problems, a method of source number estimation using delay correlation preprocessing is proposed. The delay correlation matrix of all received data is calculated to reduce the influence of noise on the estimation performance. The eigenvectors are then grouped according to the grouping rules so that different kinds of characteristic spaces can be obtained, one containing noise eigenvectors and the other containing both noise and signal eigenvectors. Finally, the delay correlation matrix is projected onto the characteristic spaces,
and thus the source number can be estimated from the projected data. Simulation and measured data show correctness and effectiveness of the algorithm.