针对多维阵列的全维自适应波束形成算法运算量大和小样本条件下性能下降问题,该文提出采用子空间重构技术的自适应波束形成算法(SRC-BF).该方法利用多维域的全维阵列数据可分维构造的特点,首先在训练样本集上估计分维阵列数据的信号子空间；然后基于张量积性质重构全维信号子空间并自适应剔除交叉项；最后采用子空间投影算法计算自适应加权矢量.理论分析和仿真结果表明该方法具有较低的运算复杂度,能有效提高自适应波束形成算法在小样本条件下的输出信干噪比.%Considering the issue that full-dimensional adaptive beam-forming takes usually a large number of sampling data and has very high computation cost by using Sample Matrix Inverse (SMI) algorithm, a new adaptive Beam-Forming algorithm based on Subspace Reconstructing (SRC-BF) is proposed in this paper. Illuminated by the multi-dimensional array data having the characteristics of reconstruction of fractal-dimension, the proposed approach is carried out in three stages. Firstly, the fractal-dimensional signal subspace of the array data is estimated using training samples; Secondly, the full-dimensional signal subspace is reconstructed by adopting the tensor product operation and the cross terms is removed adaptively; Finally, the beam-forming weight vector is deduced by the subspace projection algorithm. Theoretical analysis and simulation results show that the method has lower computational complexity and can effectively improve the output Signal-to-Interference-plus-Noise Ration (SINR) with the small sample support.
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