摘要
为提高自适应波束形成在非理想因素情况下的鲁棒性,提出一种改进狮群支持向量机鲁棒波束形成算法。结合支持向量机相关理论,构建标准支持回归波束形成优化模型;针对传统狮群算法寻优效率不高以及容易陷入局部最优解的问题,将灰狼算法中头狼的位置更新方式、萤火虫算法中的吸引力机制、差分进化算法中的变异操作引入传统狮群算法的更新方式中来提高算法的寻优性能;通过改进狮群算法对所构建的标准支持回归波束形成优化模型进行寻优求解。仿真结果表明,无论导向矢量是否失配,所提算法在高信噪比、干扰信号个数较少的情况下具有良好的鲁棒性。
To improve the robustness of adaptive beamforming under non-ideal conditions,an improved lion support vector machine robust beamforming algorithm was proposed.A standard support regression beamforming optimization model was constructed based on the theory of support vector machines.On the other hand,in view of the problem that it is easy to fall into the local optimal solution with low optimization efficiency of the traditional lion swarm algorithm,the position update method of the head wolf in the gray wolf algorithm,the attraction mechanism in the firefly algorithm,and the mutation operation in the diffe-rential evolution algorithm were introduced into the update mode of the traditional lion swarm algorithm to improve the optimization performance of the algorithm.The improved lion swarm algorithm was used to solve the standard supported regression beamforming optimization model.The simulation results show that the improved algorithm has good robustness in the case of high signal to noise ratio and fewer interference signals,no matter the steering vector is mismatched or not.
作者
刘政伟
崔琳
张熠鑫
LIU Zheng-wei;CUI Lin;ZHANG Yi-xin(School of Electronic Information,Xi’an Polytechnic University,Xi’an 710600,China;School of Marine Science and Technology,Northwestern Polytechnical University,Xi’an 710072,China;College of Mechanical Engineering,Donghua University,Shanghai 201600,China)
出处
《计算机工程与设计》
北大核心
2023年第3期770-776,共7页
Computer Engineering and Design
基金
国家自然科学基金青年科学基金项目(61901347)。
关键词
信号检测
波束形成
导向矢量
支持向量机
改进狮群算法
最优解
鲁棒性
signal detection
beamforming
steering vector
support vector machine
improved lion swarm algorithm
the optimal solution
robustness
