基于经验模态分解的宽带阵列DBF研究
作者:
作者单位:

作者简介:

通讯作者:

基金项目:

军理工大学预研基金资助项目(2009TX15)

伦理声明:



A Study of Mode Domain LCMV Algorithm Based on Empirical Mode Decomposition
Author:
Ethical statement:

Affiliation:

Funding:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    已有研究对宽带阵列信号的子带分解处理中子带划分是固定的,各子带内信号数分布不均衡。该文研究了子带自适应划分的模态域宽带自适应阵列算法,提出将经验模态分解(EMD)融合于线性约束最小方差(LCMV)算法中,EMD把多分量信号自适应地分解成多阶内蕴模态函数(IMF),突出了信号的局部瞬间特征,两者结合既满足了LCMV的窄带信号要求又充分发挥了EMD自适应的特点,解决了阵元数目受到严格限制时,宽带阵列处理自由度不足、抗干扰性能下降的问题,在强干扰环境和各信号强度相差较大时也能有效检测出较弱信号。与基于FFT的子带分割相比,无需预先划分模态函数的频段,阵元数目受限时增加了处理自由度。仿真表明3阵元强干扰条件下改善增益11 dB。

    Abstract:

    Sub band array processing for wideband adaptive array is proposed in references,but the sub band segments is fixed so the signals number distribution in each segment is unbalanced,the empirical mode decomposition(EMD) domain linearly constrained minimum variance(LCMV) algorithm for wideband array is proposed based on adaptive band segmentation,the EMD decompose multicomponent signals into many intrinsic mode function(IMFs) and focus on signals’ local characteristic, Combining IMF characteristic and meeting the narrow band request of LCMV,as well as taking fully the advantage of adaptation. The problems of anti jamming performance degradation and insufficient processing freedom degrees are settled for wideband adaptive array in the limited elements situation.The detection of weaker target is realized effectively in strong interference environment. Compared with the traditional FFT sub band,the proposed is applicable to wideband limited elements adaptive array with no prior frequency partition,increasing the array processing freedom and improving gain performance by 11 dB at 3 elements and in strong interference situation.Simulations are conducted to indicate the validity.

    参考文献
    相似文献
    引证文献
引用本文

杨明,周顺.基于经验模态分解的宽带阵列DBF研究[J].压电与声光,2012,34(4):627-630. YANG Ming, ZHOU Shun. A Study of Mode Domain LCMV Algorithm Based on Empirical Mode Decomposition[J]. PIEZOELECTRICS AND ACOUSTOOPTICS

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2012-09-25
  • 出版日期: