相关噪声下基于对角加载的相干信源DOA估计算法

针对更为广泛的空间相关噪声背景下相干信源的DOA估计问题，提出了一种对角加载的改进空间差分平滑（diagonal loading improved spatial smoothing difference, DL ISDS）算法。该算法利用均匀线阵协方差矩阵的Toeplitz分解特性进行差分运算对消相关噪声，将独立和相干信源分开分辨，重复利用阵列接收数据，可分辨更多信源，相比常规谱估计算法，DL ISDS算法具有更强的信源过载能力及阵元节省能力；另外，该算法无需预知或专门估计相干信号源数及特征值分解，可明显减小算法的运算量和复杂度。计算机仿真结果证明了DL ISDS算法理论的正确性和有效性。

DOA estimation algorithm for coherent signals based on diagonal loading in presence of correlated noises

To solve the problem of DOA estimation for coherent signals in the presence of spatial correlated noi-ses, an algorithm, called the DL-ISDS (diagonal loading-improved spatial smoothing difference), is proposed. By ex-ploiting the property of Teoplitz decomposition of the covariance matrix, the DL-ISDS method can eliminate correlated noises and resolve the uncorrelated and coherent signal separately. In this way the output data of the array are used re-peatedly, and more sources can be estimated. Compared with the conventional methods, the DL-ISDS can resolve more sources using the same number of sensors. In addition, the algorithm could skip sources number estimation and EVD (eigenvalue decomposed), so it has smaller computational complexity. The computer's simulation results verify the correctness and effectiveness of the proposed DL-ISDS method.