基于MUSIC算法的矢量水听器阵源方位估计

DOA estimation based on MUSIC algorithm using an array of vector hydrophones

矢量水听器阵的每个阵元同时测量声场中的声压量和质点振速的3个正交分量,相对于声压水听器阵来说,矢量阵获取声场中更多的信息.利用矢量阵所获得的速度场的信息可去除目标方位估计中的180°模糊.多重信号分类(MUSIC)算法是通过对数据协方差矩阵进行本征分解获得信号空间谱估计的方法.本文采用矢量水听器均匀线阵研究了利用MUSIC算法对声源进行方位估计,以提高对源方位的估计精度.仿真结果表明,在SNR=10dB的条件下,相对于常规波束形成器输出,MUSIC空间谱的主波束宽度锐化了30°左右,旁瓣降低了16dB左右,利用MUSIC算法可提高对源的定向精度及对多目标的分辨能力.

Each element of vector-hydrophone array measures the acoustic pressure and three orthogonal components of acoustic particle velocity in a sound field. The main advantage of these vector hydrophones over traditional scalar hydrophones is that they make use of more available acoustic information. Thus the 180° ambiguity in the DOA estimation can be removed using the information of acoustic particle velocity of a sound field.MUSIC (MUltiple SIgnal Characterization) is a special spectral estimation method based on the eigen decomposition of the sample covariance matrix.In this paper, the DOA of sources are estimated using MUSIC algorithm by a vector-hydrophone ULA(Uniform Linear Array) to improve the DOA estimation resolution. The simulation results indicate that a sharper main beam and higher resolution of DOA estimation can be obtained using MUSIC algorithm, and the ability to distinguish multi-source is also improved.