摘要
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.
出处
《声学技术》
CSCD
2013年第6期464-468,共5页
Technical Acoustics
基金
国家自然科学基金(51279043
51209059
61201411)
水声技术国家级重点实验室基金资助项目(9140C200203110C2003)
关键词
声矢量阵
质点速度场平滑
矩阵平方平滑
相干源
波达方向估计
acoustic vector array
particle velocity field smoothing
matrix square smoothing
coherent source
DOA estimation