快速酉ESPRIT方法

酉ESPRIT方法虽然能以较小的计算代价改善标准ESPRIT方法的参数估计性能 ,但仍需计算实数矩阵的奇异值。提出了一种基于酉变换的信号子空间快速近似方法。该方法在数据矩阵酉变换的基础上 ,有理近似估计信号子空间 ,利用均匀线阵的中心对称性 ,使矩阵运算变为实数运算 ,无需奇异值分解 ,大大减小了运算量。仿真实验表明 ,该方法的参数估计性能接近酉ESPRIT方法 ,优于标准ESPRIT和有理近似ESPRIT方法。

A Fast Algorithm for Unitary ESPRIT

Although unitary ESPRIT improves the parameter estimation of standard ESPRIT algorithm with a reduced computational burden, it requires the singular value decomposition (SVD) of the real-valued covariance matrix. A method to estimate signal and noise subspaces based on rational approximation of the covariance matrix without costly SVD is presented. The computational complexity is also reduced significantly by unitary transformation from the sampled data matrix to the real-valued matrix when centro-symmetric array configurations are used. Furthermore, the forward-backward averaging leads to an improved performance, especially for correlated signals. Simulations confirm that the new algorithm has a performance comparable to that of unitary ESPRIT and outperforms both standard and rationally-approximated ESPRIT algorithms.