Application of Second-Order Cone Programming Theory to Robust Adaptive Beamforming
Due to the array steering vector errors and small-sample errors and so on,the performance of the Standard Capon Beamformer(SCB)may become worse than that of the Conventional Beamformer(CBF)in practical engineering applications.Nowadays,almost all existing algorithms to improve the robustness of SCB utilize the steepest descent method,conjugate gradient method,least mean squares(LMS)method and so on.However,most of them have the slow convergence and inefficient computation.Aiming at the above problems,this paper presents a unified process to solve them by the second-order cone programming(SOCP)theory,which can not only enhance the convergence speed but also improve the calculation accuracy so as to overcome the shortcomings of the previous optimization solutions effectively.It turns out that most adaptive beamforming algorithms involve a non-convex problem which is minimization of a quadratic function subject to infinitely many non-convex quadratic constraints.In this paper,it is shown that the proposed algorithm can be reformulated in a convex form as the so-called SCOP and solved efficiently(in polynomial time)using the well-established interior point method.Finally,computer simulations show the algorithms excellent performance as compared with existing adaptive beamforming algorithms via the computer simulation examples.
Second-order cone programming Robust adaptive beamforming Sedumi
Rong-Yi Zhang Hai-Yan Song
Mechitrone Engineering College,Heilongjiang Institute of Technology,Harbin,China School of Electrical and Information Engineering,Heilongjiang Institute of Technology,Harbin,China
国际会议
The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)
福州
英文
405-412
2015-05-08（万方平台首次上网日期，不代表论文的发表时间）