改进的双约束稳健Capon波束形成算法

李立欣; 白童童; 张会生; 包涛; 申礼斌

doi:10.11999/JEIT151213

改进的双约束稳健Capon波束形成算法

doi: 10.11999/JEIT151213

基金项目:

中央高校基本科研业务费专项资金(3102014JCQ01052, 3102014KYJD034, 3102014KYJD040)，陕西省自然科学基础研究计划资助项目(2016JM6062)，中国航天科技集团公司航天科技创新基金资助项目

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出版历程
- 收稿日期: 2015-11-03
- 修回日期: 2016-03-31
- 刊出日期: 2016-08-19

Improved Double Constraint Robust Capon Beamforming Algorithm

Funds:

The Fundamental Research Funds for the Central Universities (3102014JCQ01052, 3102014KYJD034, 3102014KYJD040), The Natural Science Foundation Research Project of Shaanxi Province (2016JM6062), The Aerospace Science and Technology Innovation Fund of CASC

摘要

摘要: 传统双约束稳健Capon波束形成算法采用牛顿迭代法求解最优加载量，存在计算精度低且运算量大的问题。该文提出一种改进的双约束稳健Capon波束形成(DCRCB)算法，该算法对信号协方差矩阵进行重构，基于期望信号导向矢量在噪声子空间的投影最优，将重构后的干扰加噪声协方差矩阵投影到噪声子空间，得到基于噪声子空间的双约束算法模型。该算法中通过模约束的辅助约束作用，将改进的双约束算法模型转化为单约束问题，最终解得最优对角加载量的解析表达式。仿真结果表明改进算法能通过调整主瓣宽度优化波束旁瓣，有效提高了抗矢量偏差的鲁棒性，同时降低了运算量。
- 自适应波束形成 /
- 双约束Capon波束形成 /
- 信号协方差矩阵重构 /
- 噪声子空间
Abstract: Traditional double constraint robust Capon beamforming algorithm uses Newton iterative method for solving the optimal loading, presenting the problems of low accuracy and large amount of computation. An improved Double Constraint Robust Capon Beamforming (DCRCB) algorithm is proposed in this paper. The algorithm reconstructs the signal convariance matrix, and by optimizing the projection of signal steering vector onto the noise subspace, it projects the reconstructed interference-plus-noise convariance matrix onto the noise subspace, obtaining the double constraint algorithm model based on the noise subspace. For the norm constraint is accessorial, the algorithm model can be converted into a single constraint issue and be solved into an analytical expression of optimal diagonal loading finally. Simulation results show that the improved algorithm can optimize the side lobe by adjusting the beam width of the main lobe, improve effectively the anti-vector error robustness, and reduce the amount of computation.
- Adaptive beamforming /
- Double constraint Capon beamforming /
- Signal convariance matrix reconstrcting /
- Noise subspace

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