Efficient 2D adaptive beamforming algorithm based on sparse array optimisation

Abstract

In this study, a 2D adaptive beamforming algorithm for sparse array is proposed. Firstly, a signal model based on matrix completion theory for adaptive beamforming in sparse array is established, which is proved to satisfy null space property. Secondly, in order to enhance the performance of reconstructing complete received signal matrix, genetic algorithm is used to optimise the sparse sampling array. Thirdly, the accelerated proximal gradient algorithm is adopted to reconstruct the complete received signal matrix. Finally, the adaptive beamforming weight is provided directly to form beam patterns, which can be obtained as a result of reconstructing complete received signal matrix. The proposed method could improve the utilisation rate of the sparse array elements and reduce the computational complexity in interference suppression. Simulation results show the effectiveness of the method.

Keywords

• array signal processing
• genetic algorithms
• gradient methods
• computational complexity
• interference suppression
• matrix algebra
• sparse array optimisation
• signal model
• matrix completion theory
• complete received signal matrix
• genetic algorithm
• sparse sampling array
• accelerated proximal gradient algorithm
• adaptive beamforming weight
• sparse array elements