AI 中文总结
针对大孔径阵列中近场高斯协方差矩阵近似的高复杂度问题，提出低复杂度二维近场高斯协方差近似框架，通过降观测表示避免完整特征分解，结合自校准谱误差估计器实现准确收敛跟踪与复杂度大幅降低。
AI 中文摘要
近场协方差矩阵是大孔径阵列中定位、协方差感知估计及线性最小均方误差（MMSE）滤波的核心，但高斯位置不确定性需要对非线性球面波导向矢量进行代价高昂的数值平均。本文提出一种用于二维近场高斯协方差近似的低复杂度框架，通过将正交协方差写为RQ = HHH，从降观测表示中获取主导协方差谱，避免了完整M×M的特征分解。进一步引入仅使用网格间主导谱差异的自校准无参考谱误差估计器，数值结果显示其具备准确的收敛跟踪能力及显著的复杂度降低效果。
英文摘要
Near-field covariance matrices are central to local- ization, covariance-aware estimation, and linear MMSE filtering in large-aperture arrays, but Gaussian position uncertainty requires costly numerical averaging of nonlinear spherical-wave steering vectors. This letter proposes a low-complexity framework for two-dimensional near-field Gaussian covariance approxima- tion. By writing the quadrature covariance as RQ = HHH , the dominant covariance spectrum is obtained from a reduced observation representation, avoiding full MxM eigendecom- position. A self-calibrated non-reference spectral-error estimator is further introduced using only grid-to-grid dominant-spectrum differences. Numerical results show accurate convergence track- ing and substantial complexity reduction.
CommentsSubmitted to transaction on signal processing 5 pages 3 figures
