J4 ›› 2012, Vol. 39 ›› Issue (4): 67-73.doi: 10.3969/j.issn.1001-2400.2012.04.013

• 研究论文 • 上一篇    下一篇

基于粒子群优化算法的功率倒置阵列

张彪1,2;王杰令1;田斌1,2;易克初1   

  1. (1. 西安电子科技大学 综合业务网理论及关键技术国家重点实验室,陕西 西安  710071;
    2. 中国电子科技集团第36所 通信对抗国防科技重点实验室,浙江 嘉兴  314033)
  • 收稿日期:2011-07-11 出版日期:2012-08-20 发布日期:2012-10-08
  • 通讯作者: 张彪
  • 作者简介:张彪(1985-),男,西安电子科技大学硕士研究生,E-mail: 88176820@163.com.
  • 基金资助:

    高等学校基本科研业务费资助项目(K50510010020);高等学校学科创新引智计划资助项目(B08038);国家自然科学基金资助项目(61101146)

Power inversion array based on particle swam optimization

ZHANG Biao1,2;WANG Jieling1;TIAN Bin1,2;YI Kechu1   

  1. (1. State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an  710071, China;
    2. National Lab. of Info. Control, No. 36 Inst., CECT, Jiaxing  314033, China)
  • Received:2011-07-11 Online:2012-08-20 Published:2012-10-08
  • Contact: ZHANG Biao

摘要:

针对功率倒置阵列采用最小均方(LMS)算法不能兼顾收敛速度和稳态误差的问题,以及采用递归最小二乘(RLS)算法运算量增大,实现复杂等缺点,提出采用时变适用度函数的粒子群优化(PSO)算法.通过引入可变的惯性因子、可变的最大速度、选择机制等操作,自适应调整阵列权系数来寻找最优权值.将此算法应用于功率倒置阵列中能有效地生成零陷抑制干扰.

关键词: 粒子群优化算法, RLS算法, LMS算法, 功率倒置阵列, 时变适应度函数

Abstract:

The convergent speed and steady-state misadjustment error can not be improved simultaneously in the power inversion array when using the least mean square(LMS) algorithm. However, the recursive least squares (RLS) algorithm used in the power inversion array also bears a burden of large computation and is realized with difficulty. To solve this problem, the particle swarm optimization (PSO) algorithm adopting the time-varying fitness function is proposed. By means of introducing such techniques as the variable inertia factor, variable maximum speed and selection mechanism, the algorithm can adjust the weight coefficients adaptively to find the best solution. The power inversion array using the PSO algorithm can place nulls to suppress jamming effectively.

Key words: particle swarm optimization algorithm, RLS algorithm, LMS algorithm, power inversion array, time-varying fitness function

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