Aim. The introduction of the full paper reviews some papers in the open literature and then proposes what we believe to be a new and better method, which is explained in sections 1 and 2. Their core consists of: ( 1 )on the basis of analyzing the SVM algorithm that uses the structural risk minimization principle, we rewrite the traditional linear constrained minimum variance cost function by introducing additional inequality constraints; (2) we adopt an iterative re-weightod least-squares procedure to reduce the high computational cost incurred by the quadratic programming technique; our SVM-basod beamforming method can enhance the robustness of optimal beamforming of array signal against random errors. To appraise our method, section 3 simulates the uniform linear array with 10 sensors in the ideal scenario of no mismatch and the actual scenario of mismattch respectively; the simulation results, given in Figs. 1 through 8, and their analysis show preliminarily that: ( 1 ) our SVM-based beamforming mehtod enhances the robustness in terms of desired signal array response errors in an ideal scenario of no-mismatch and an actual scenario of mismatch respectively; (2) it achieves satisfactory robustness especially under the condition of high signal to noise ratio (SNR) values or with numerous interference signals. Finally, our SVM-basod beamforming method provides a new and effective approach to enhancing the robustness of a beamformer.%针对传统的最优波束形成对基阵的微小扰动非常敏感,使得最优波束形成器在导向向量存在误差时性能下降.为了改善最优波束形成器的鲁棒性,在对基于结构风险最小化原理的支持向量机(Support Vector Machine,SVM)算法分析的基础上,通过纳入附加的不等式约束来修改传统的线性约束最小方差价值函数,提出基于支持向量机的鲁棒波束形成方法.同时,为了减轻二次规划技术所带来的高计算成本,凸优化过程采用迭代重加权最小二乘算法来实现.与传统的最优波束形成算法相比,新方法能够提高最优波束形成对误差的鲁棒性.数值仿真实验表明:在无失配的理想情形和有失配的实际情形下,基于支持向量机的波束形成算法在期望信号阵列响应误差方面增加了鲁棒性,特别是在高信噪比或干扰信号数目较多的情况下,取得了满意的效果,为提高波束形成器的鲁棒性提供了一种新的有效途径.
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