声矢量阵快速子空间方位估计算法
CSTR:
作者:
作者单位:

(1.哈尔滨工程大学 水声技术重点实验室, 150001 哈尔滨; 2.哈尔滨工程大学 水声工程学院, 150001 哈尔滨)

作者简介:

梁国龙(1964—),男,教授,博士生导师.

通讯作者:

安少军,asj78@163.com.

中图分类号:

TN911

基金项目:

国家自然科学基金(3,9,61201411);海军装备预研项目基金(1011204030104);水声技术国家级重点实验室基金(9140C200203110C2003).


Fast subspace DOA estimation algorithm based on acoustic vector sensor array
Author:
Affiliation:

(1.Science and Technology on Underwater Acoustic Laboratory,Harbin Engineering University,150001 Harbin, China; 2. College of Underwater Acoustic Engineering, Harbin Engineering University, 150001 Harbin, China)

Fund Project:

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

    针对声矢量阵高分辨方位估计算法运算量大的问题,基于声压振速联合信息处理,提出了一种快速的声矢量阵高分辨方位估计算法.该算法选择参考阵元的电子旋转矢量作为期望信号,运用多级维纳滤波器(MSWF)对信号子空间进行快速估计,不需要计算声矢量阵的互协方差矩阵,不用进行特征值分解,从而大大缩减了计算量.另外,该算法基于矢量传感器声压与振速的相干性原理,充分利用了声压振速组合抗干扰能力,有效抑制了各向同性噪声.理论分析和计算机仿真表明,该算法在拥有良好DOA估计性能的同时,大大减小计算量.

    Abstract:

    Against the problem of huge computation of high-resolution DOA estimation algorithm using acoustic vector sensor array, a fast high-resolution DOA estimation algorithm was proposed based on the combination processing of pressure and particle velocity. The algorithm selected the electronic rotation vector of the reference element as the desired signal and MSWF(multi-stage Wiener filter) was used to estimate the signal subspace, which greatly reduced the amount of computation because it did not need to calculate the cross-covariance matrix of acoustic vector sensor array and Eigen value decomposition. The algorithm is based on the principle of coherency between pressure and particle velocity, which can suppress interference well in isotropic noise field. Theoretical analysis and computer simulations show that the algorithm has good performance of DOA estimation while it greatly reduces the amount of computation.

    参考文献
    相似文献
    引证文献
引用本文

梁国龙,张柯,安少军,范展.声矢量阵快速子空间方位估计算法[J].哈尔滨工业大学学报,2014,46(7):76. DOI:10.11918/j. issn.0367-6234.2014.07.013

复制
相关视频

分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2013-07-30
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2014-07-30
  • 出版日期:
文章二维码