非一致性噪声条件下的特征空间波束形成

由于水声环境的复杂性，阵列的噪声分布可能是非一致性的。当阵元噪声功率各不相同时，阵列协方差矩阵特征分解得到的特征子空间与真实目标的特征子空间之间存在误差，导致特征子空间波束形成算法的性能衰减。文章提出了一种新的非一致性噪声条件下特征子空间的估计方法，将阵列协方差矩阵对角线置0，进行特征分解估计的特征子空间将不受阵元噪声非一致性的影响。将该方法应用到特征空间波束形成算法，提高了非一致性噪声条件下特征空间波束形成算法的方位分辨能力。仿真和实验结果验证了所提方法的可行性和有效性。

Eigenspace based beamforming in the presence of nonuniform noise

The noise distribution of the array signal may be inconsistent due to the complexity of the underwater acoustic environment. When the noise powers of the array elements are different, there will be an error between the eigen subspace of the real target and the eigen subspace obtained by the eigen decomposition of the array covariance matrix, thus resulting in the degradation of the performance of the beamforming algorithm utilizing the eigen subspace. In this paper, a new eigen-subspace estimation method, realized by setting the diagonal of the array covariance matrix as zero, is proposed to overcome the discrepancy of the eigen-subspace for eigen-decomposition estimation under non-uniform noise conditions. By applying this method to the characteristic space beamforming algorithm, the azimuth resolution ability of the characteristic space beamforming algorithm can be highly improved under inconsistent noise conditions. Simulation and experimental results verify the feasibility and effectiveness of the method proposed in this paper.