Generalized Sidelobe Canceller Based Antenna Array Beamformer with Reduced Computational Complexity

For adaptive beamforming, the linearly constrained minimum variance (LCMV) beamformer is widely considered in the literature. However, the computational complexity required by an LCMV beamformer is increased significantly as the number of its sensors increases. To alleviate this difficult, we present a method to reduce the number of adaptive weights required by a generalized sidelobe canceler (GSC) based adaptive array beamformer without significantly sacrificing its performance. To achieve this goal, the proposed method chooses the eigenvectors corresponding to the most significant eigenvalues of the correlation matrix associated with the output of the signal blocking matrix. Based on these selected eigenvectors, a novel signal blocking matrix is constructed to effectively reduce the dimension of the adaptive weight vector required for GSC based adaptive beamforming. As a result, the performance of the proposed method can approach to that of the conventional GSC based adaptive array beamformer. The analysis of the computational complexity and the simulation results for showing the effectiveness of the proposed method are also presented.