LAUSR

Abstract Aiming at the problem of high complexity and poor real-time performance of the traditional minimum variance (MV) algorithm, a low-complexity minimum variance algorithm combined with power method is proposed. Firstly, the echo data is transformed into beam domain by discrete cosine transform and the dimension reduction parameter is determined according to the data of scanning lines. Secondly, the maximum eigenvalue and corresponding eigenvector of sample covariance matrix are obtained by the power method to reduce the complexity of eigenvalue decomposition. Finally, by ignoring low-energy echo signal, the inversion of covariance matrix can be simplified to construct a new weighted vector, which can reduce the complexity of MV. The Field II simulation results show that the proposed algorithm has better resolution, contrast ratio and efficiency than the traditional MV algorithm, and outperforms the minimum variance algorithm based on eigenvalue decomposition (ESBMV) in resolution and efficiency.