阵列波束优化的标准支持向量回归

Standard support vector regression of optimized array beamforming

针对支持向量机在解决小样本、非线性和高维模式处理问题上的优势,对比支持向量回归与波束优化理论,修正支持向量回归价值损失函数.分析支持向量回归波束优化的基本方法及其应用条件,建立标准支持向量回归波束优化模型,建立基于标准支持向量回归波束形成器的优化模型及具体实现过程并进行数值仿真实验.结果表明,在不同的阵型、价值损失函数、数据样本和不同的信噪比下,基于标准支持向量回归的波束形成器在指向性和旁瓣级等性能指标上均取得了较好的效果,为波束形成器的优化设计提供了有效且可行的方法.

For the Advantages of Support Vector Machines,such as the small sample,nonlinear and high dimension,it compares the support vector regression algorithm with beam optimization theory,and modifies the cost function of support vector regression.The basic methods and its application conditions which support vector regression beam optimizing ae anahped,and its model is set up.Then the optimization model of the beamformer is established,the optimization model and the concrete implementation process of beamformer based on the stand support vector regression is discussed,and the numerical simulation experiments are done.The results show that for different arrays,cost functions,data samples and SNR,the beamformer has achieved good results,such as directivity,and sidelobe level.Compared with the conventional beamforming,it provides an effective and feasible method for the optimization design of beamformer.