直接数据特征值分解的相干源DOA估计
DOA Estimation of Coherent Signals Based on DD-EVD
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摘要: 提出一种基于均匀圆阵单次快拍数据的相干信源波达方向(direction of arrival,DOA)估计方法——直接数据特征值分解(direct data eigenvalue decomposition,DD-EVD)法. 算法通过模式空间转换将均匀圆阵虚拟为均匀线阵,再直接利用波束空间的快拍数据,构造一个Toeplitz矩阵,并对矩阵按阵列流形分解. 理论推导证明,矩阵的秩得到恢复,只与入射信号个数有关. 对该矩阵进行特征值分解可得到正确的信号子空间和噪声子空间,进而完成相干信源DOA估计. 算法使用单次快拍数据构造矩阵,适合非平稳信号参数的估计,同时不需要快拍累计和相关运算,降低了计算复杂度. 仿真结果验证了算法的有效性.Abstract: Based on uniform circular arrays (UCA), a direction of arrival (DOA) estimation algorithm of coherent signals (DD-EVD) with single snapshot data was proposed. The algorithm used mode excitation to transform the manifold matrix of UCA into uniform linear array. Then, a Toeplitz matrix which was reconstructed with the data vectors of beam space was decomposed in accordance with array manifold. It was proved that the rank of this Toeplitz matrix was regained and determined merely by the number of incident sources. Accurate signal subspace and noise subspace can be acquired by performing eigenvalue decomposition of the matrix. Combined with the subspace kind algorithms, the DOA of coherent signals was estimated. Because of using single snapshot data to reconstruct matrix, the algorithm does not need correlation calculation and cumulation of snapshots. In view of this, the algorithm is intended to estimate the DOA of non-stationary signals and the complexity of the algorithm is reduced. Finally, the simulation verifies the effectiveness.
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