Adaptive Beam-forming Algorithm with Subspace Reconstructing

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.