Improved Gram-Schmidt orthogonalization beam-forming algorithm based on data preprocessing

When the desired signal is mixed in the training data, the conventional Gram-Schmidt orthogonalization beam-forming algorithm will result in the desired signal cancellation. In this paper, an improved Gram-Schmidt orthogonalization beam-forming algorithm based on data preprocessing was proposed to resolve the desired signal cancellation. In the proposed algorithm, the training data are firstly preprocessed to remove the desired signal by the designed block matrix, then the corresponding covariance matrix was estimated, and the interference subspace was reconstructed by Gram-Schmidt orthogonalization of the columns of the covariance matrix. Finally, the adaptive weight vector was obtained by orthogonally projecting the quiescent weight vector into the interference subspace. Moreover, the orthogonalization adaptive threshold of the covariance matrix was re-designed for accurate interference subspace estimation. Simulation results show that the output signal to interference plus noise ratio (SINR) of the proposed algorithm is improved above 2 dB comparing with the current Gram-Schmidt orthogonalization methods.