基于子空间分解的阵列流形向量估计新方法

A New Subspace Decomposition Based Array Manifold Estimation Algorithm

实际阵列装配完成后的阵列流形向量与理论值存在偏差,这种偏差会导致阵列预设波束图的旁瓣升高、阵列高分辨算法的性能下降,严重影响阵列的实际应用。实际中先依据估计的部分实际阵列流形向量选取合适的误差模型,再根据模型得到逼近实际的阵列流形向量。现有的实际阵列流形向量估计方法有直接定义法和最小二乘法,这两种方法计算复杂度很高且估计精度随快拍数波动。对此本文给出一种新的阵列实际流形向量估计方法,它利用阵列接收数据协方差矩阵的信号子空间与阵列流形向量张成空间相同的特性来估计阵列的实际幅度相位响应,结合估计的波达方向,最后得到实际的阵列流形向量。仿真结果表明,本文所提方法比现有的两种估计方法估计精度高一倍且计算复杂度降低了一个数量级。

The manifold vector (MV) of an assembled array usually differs from its theoretical value. The deviation raises the sidelobe levels of the beampattern and degrades the performance of high-resolution array processing algorithms, hence limiting practical application of the array. A good approximation to the MV can be obtained on the basis of a properly chosen parametric array error model, with a partially estimated MV for bootstrapping. MV estimation algorithms such as definition-based method (DB) and least square method (LS) are computationally complex, and their accuracy is subject to the number of snapshots available for the estimation. We propose a new MV estimation algorithm that exploits the equivalence between the signal subspace of the covariance matrix and the space spanned by the MV to estimate the magnitude-phase response of an array. We can derive the MV by combining the magnitude-phase response with the estimated direction of arrival (DOA). Simulation results demonstrate that the proposed method is twice as accurate as DB and LS, and its computational complexity is reduced by an order of magnitude.