一种数值稳健且低复杂度的信号子空间估计新方法

 该文提出了一种数值稳健且低复杂度的信号子空间估计新方法。该方法通过多级维纳滤波器前向迭代构造观测数据协方差矩阵三对角化的转换矩阵，其列向量为信号子空间的一组正交基。与传统的相关相减结构结构相比，该文的多级维纳滤波器前向迭代通过Householder酉变换实现，显著增强了有限精度运算中信号子空间基向量的正交性，提高了数值稳健性。此外，基于Householder矩阵的酉性质和矩阵后向累积提出了一种转换矩阵的快速计算方法，降低了计算复杂度。计算机仿真结果验证了该方法的数值稳健性和计算效率。

A Numerically Robust and Low-complexity Method of Signal Subspace Estimation

A numerically robust and low-complexity method of signal subspace estimation is proposed in the paper. The transform matrix to tridiagonalize the covariance matrix of observation data is constructed in the forward recursion of multistage Wiener filter (MSWF), and its columns span the signal subspace. Compared with the traditional method of correlation subtractive structure, the forward recursion in the method is implemented with the Householder unitary transform. Therefore, it strengthens significantly the orthogonality of basis vectors in the signal subspace and improves the numerical robustness, especially in the finite-precision implementation. Besides, a method of calculating the transform matrix is proposed to reduce the computational complexity based on the unitary property of the Householder matrix and backward accumulation of matrices. Finally, simulation results demonstrate the numerical robustness and computational efficiency of the proposed method.