系统工程与电子技术

• 电子技术 • 上一篇    下一篇

ULA中基于LS-ICA的短时高效DOA估计

牛德智1,2, 陈长兴1, 陈婷3, 陈强1, 任晓岳1, 蒋金1, 程蒙江川1   

  1. 1. 空军工程大学理学院, 陕西 西安 710051; 2. 西安通信学院, 陕西 西安 710106;
    3. 西安邮电大学电子工程学院, 陕西 西安 710061
  • 出版日期:2015-10-27 发布日期:2010-01-03

Quick DOA estimation with high efficiency based on LS-ICA in ULA

NIU De-zhi1,2, CHEN Chang-xing1, CHEN Ting3, CHEN Qiang1,REN Xiao-yue1, JIANG Jin1, CHENGMENG Jiang-chuan1   

  1. 1. College of Science, Air Force Engineering University, Xi’an 710051, China; 2. Xi’an Communications Institute, Xi’an 710106, China; 3. School of Electronic Engineering, Xi’an University of Posts & Telecommunications, Xi’an 710061, China  
  • Online:2015-10-27 Published:2010-01-03

摘要:

研究了均匀线阵(uniform linear array, ULA)的波达方向(direction of arrival,DOA)估计问题,提出了一种能充分利用阵列结构上来波数据的信号处理方法进行角度估计。所提方法的关键是采用天线阵列因子的实部来构造新的信号接收模型,进而通过独立分量分析(independent component analysis,ICA)对接收信号进行盲分离,用最小二乘(least square,LS)法求解入射信号的角度,同时给出了天线阵元间距应满足的条件。仿真表明,与多重信号分类(multiple signal classification,MUSIC)方法相比,本文方法能够适应阵元间距的变化,在低信噪比(signal-to-noise ratio,SNR)时估计性能以及角度分辨力均较优,通过一步粗估计就可获得较高精度且具有实时性,均方根误差(root mean square error,RMSE)向克拉美-罗界(Cramer-Rao bound,CRB)的逼近程度也说明了新方法的优势,此外还可以采用细估计来提高估计精度。

Abstract:

The problem of direction of arrival (DOA) estimation based on uniform linear array (ULA) is studied. A signal processing method by making full use of the receiving data in the antenna array is proposed to estimate angles of spatial signals. The key point is to construct a new model of received signals by adopting the real part of the antenna array cell, next to implement blind source separation for received signals by independent component analysis (ICA), and solve incident angles by the least square (LS) method. The requirement for antenna array cell internal is given in the process of the algorithm. Simulation experiments show that compared with the multiple signal classification (MUSIC) method, the new method is more suitable for adjusting the antenna array cell internal, has better estimation performance in low signal-to-noise ratio (SNR) and degree distinguishing capacity, and achieves high precision by onestep crude estimation and good real time performance. Also, its merit is illustrated by the root mean square error (RMSE) curve and Cramer-Rao bound (CRB) characteristics. Furthermore, estimation precision can be improved by refined estimation course.