Fast and accurate covariance matrix reconstruction for adaptive beamforming using Gauss-Legendre quadrature

• 作者: LIU Shuai ZHANG Xue YAN Fenggang WANG Jun JIN Ming
• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2021,（1）
• 所属期刊栏目: ELECTRONICS TECHNOLOGY
• 页码范围: 38-43
• 页数: 6页
• 语种: 英文
• DOI: 10.23919/JSEE.2021.000005

摘要

Most of the reconstruction-based robust adaptive beamforming (RAB) algorithms require the covariance matrix re-construction (CMR) by high-complexity integral computation. A Gauss-Legendre quadrature (GLQ) method with the highest alge-braic precision in the interpolation-type quadrature is proposed to reduce the complexity. The interference angular sector in RAB is regarded as the GLQ integral range, and the zeros of the three-order Legendre orthogonal polynomial is selected as the GLQ nodes. Consequently, the CMR can be efficiently obtained by simple summation with respect to the three GLQ nodes without integral. The new method has significantly reduced the complex-ity as compared to most state-of-the-art reconstruction-based RAB techniques, and it is able to provide the similar perform-ance close to the optimal. These advantages are verified by nu-merical simulations.