1. 中国科学院信息工程研究所， 北京 100193； 2. 中国科学院声学研究所， 北京 100190;3. 中国科学院大学， 北京 100190
出版日期:2019-08-28发布日期:2019-09-10通讯作者:汪永明，男，正高级工程师，博士生导师，电话(Tel.): 010-82547620；E-mail: wangyongming@iie.ac.cn.作者简介:李冰(1978-)，男，湖北省恩施市人，副研究员，目前主要从事水声通信和水声信号处理研究.Multiple Signal Classification Beam-Forming Method Based on Time Domain Analysis
LI Bing 1,2,3,WANG Yongming 1,3,HUANG Haining 2,31. Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100193, China;2. Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;3. University of Chinese Academy of Sciences, Beijing 100190, China
Online:2019-08-28Published:2019-09-10摘要/Abstract
摘要： 针对频域多重信号分类(MUSIC)算法估计子空间的不稳定性问题，提出了一种基于时域解析估计子空间的MUSIC(TAMUSIC)波束形成方法.通过Hilbert变换将各阵元时域实数据转变为复解析数据，在时域构建经过时延后的协方差矩阵，利用特征分解求取噪声子空间，并利用噪声子空间自身的正交特性获得来波方向波束.数值仿真及实测数据处理结果表明，与频域MUSIC波束形成方法相比，TAMUSIC波束形成方法可以稳定获取快速运动目标的噪声子空间和来波方向波束，使波束旁瓣级最少降低3dB，能够有效检测运动目标，且无虚假目标和波束分裂现象，并提高MUSIC波束形成方法在工程应用中的稳定性.
关键词: 多重信号分类波束形成； 快速运动目标； 时域解析
Abstract: For the instability problem of multiple signal classification (MUSIC) beam-forming estimating subspace in frequency domain, a multiple signal classification beam-forming method based on time-domain analysis (TAMUSIC) was proposed. Firstly, the complex analysis data were obtained from the time domain real data by the Hilbert transformation. Secondly, the covariance matrix was constructed in time domain after the time delay, and the noise subspace was statistically obtained by eigen-decomposition. Finally, the beam was obtained by the orthogonal properties of the noise subspace in direction of arrival. The processed results of numerical simulation and measured data show that under the case of fast moving target, compared to the MUSIC beam-forming method, TAMUSIC beam-forming method can statistically get the noise subspace and the beam in direction of arrival for fast moving target, improve the side-lobe level more than 3dB. It effectively detects the fast moving target, and has no phenomenon of false target and split beam. It also improves the stability of MUSIC beam-forming method in practical project.
Key words: multiple signal classification (MUSIC) beam-forming; fast moving target; time domain analysis
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