声矢量阵相干源方位估计算法

DOA estimation of coherent signals based on acoustic vector array

基于声矢量阵的质点速度场平滑(Particle Velocity Field Smoothing,PVFS)算法是一种有效的解相干算法,但是当存在大量相干源时,该算法性能急剧下降甚至失效。在PVFS算法的基础上,提出了矩阵平方平滑(Matrix Square Smoothing,MSS)-PVFS算法,该算法是对PVFS算法的改进,通过对PVFS算法构造的数据协方差矩阵进行平方、矩阵分块以及矩阵块间交叉相乘等数学运算,增强了PVFS算法解相干的能力,并大大增加了其分辨相干源的数目。计算机仿真结果表明,MSS-PVFS算法的效果与空间平滑(Spatial Smoothing,SS)-PVFS算法大致相同,但在低信噪比条件下,该方法具有更高的DOA估计精度。

PVFS （Particle Velocity Field Smoothing） algorithm based on acoustic vector sensor array is an effective decorrelation algorithm. However, the performance will seriously degrade or even fails when there are a large number of coherent sources. Based on PVFS, the MSS （Matrix Square Smoothing） algorithm is proposed as the amelioration of PVFS. The proposed algorithm first squared and partitioned the data covariance matrix constructed by PVFS. Then, the partitioned matrix was cross-multiplied by each other. Finally, the decorrelation ability of PVFS algorithm was enhanced and more coherent sources can be distinguished. Computer simulation indicated that the proposed algorithm achieved a similar performance as the Spatial Smoothing PVFS algorithm. Moreover, the DOA estimation accuracy of the proposed algorithm was much higher when the signal-to-noise ratio was low.