CAJ | 학술논문

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。
기우성시량진적질점속도장평활(Particle Velocity Field Smoothing, PVFS)산법시일충유효적해상간산법，단시당존재대량상간원시，해산법성능급극하강심지실효。재PVFS산법적기출상，제출료구진평방평활(Matrix Square Smoothing, MSS)-PVFS산법，해산법시대PVFS산법적개진，통과대PVFS산법구조적수거협방차구진진행평방、구진분괴이급구진괴간교차상승등수학운산，증강료PVFS산법해상간적능력，병대대증가료기분변상간원적수목。계산궤방진결과표명，MSS-PVFS산법적효과여공간평활(Spatial Smoothing, SS)-PVFS산법대치상동，단재저신조비조건하，해방법구유경고적DOA고계정도。
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.