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朱晓春, 朱中锐, 张旭, 时胜国. 柱形障板条件下声矢量圆阵相位模态域目标方位估计[J]. 声学学报, 2024, 49(2): 274-285. DOI: 10.12395/0371-0025.2022141
引用本文: 朱晓春, 朱中锐, 张旭, 时胜国. 柱形障板条件下声矢量圆阵相位模态域目标方位估计[J]. 声学学报, 2024, 49(2): 274-285. DOI: 10.12395/0371-0025.2022141
ZHU Xiaochun, ZHU Zhongrui, ZHANG Xu, SHI Shengguo. Direction-of-arrival estimation based on phase modal space for a circular acoustic vector-sensor array on the cylindrical baffle[J]. ACTA ACUSTICA, 2024, 49(2): 274-285. DOI: 10.12395/0371-0025.2022141
Citation: ZHU Xiaochun, ZHU Zhongrui, ZHANG Xu, SHI Shengguo. Direction-of-arrival estimation based on phase modal space for a circular acoustic vector-sensor array on the cylindrical baffle[J]. ACTA ACUSTICA, 2024, 49(2): 274-285. DOI: 10.12395/0371-0025.2022141

柱形障板条件下声矢量圆阵相位模态域目标方位估计

Direction-of-arrival estimation based on phase modal space for a circular acoustic vector-sensor array on the cylindrical baffle

  • 摘要: 提出了基于横向、纵向和对角排列的声压振速联合处理协方差矩阵构造的相位模态域最小方差无畸变响应(MVDR)目标方位估计方法。该方法利用弹性柱障散射声场模型, 根据声矢量圆阵相位模态变换理论, 构造声压与不同振速分量的互协方差矩阵, 并进行相加、纵向、横向和对角排列得到扩展互协方差矩阵, 将扩展互协方差矩阵分解重构后进行MVDR目标方位估计。理论分析与仿真结果表明, 纵向排列互协方差矩阵具有更好的方位估计性能, 相加互协方差矩阵具有较低的背景谱, 对角和横向排列相对纵向排列较差。水池试验和湖试结果进一步验证了纵向排列互协方差矩阵具有较其他3种方法更好的方位估计性能。

     

    Abstract: A method is introduced for estimating the target azimuth in the phase mode domain using the minimum variance distortionless response (MVDR) technique. The approach relies on joint processing of sound pressure and velocity through a covariance matrix, arranged predominantly in vertical, horizontal, and diagonal configurations. Employing an acoustic field model with an elastic cylindrical baffle, this method transforms the phase mode of an acoustic vector circular array. Subsequently, mutual covariance matrices of sound pressure and velocity components from distinct points are generated. Extensive cross-covariance matrices are calculated with additive summation, arranged vertically, horizontally, and diagonally, then decomposed to achieve target azimuth estimation using MVDR. Theoretical analysis and numerical simulations indicate that the vertically arranged cross-covariance matrix delivers the most accurate azimuth estimation, while the additive covariance matrix exhibits the least background noise. Nevertheless, the horizontal and diagonal arrangements show inferior performance compared to the vertical arrangement. Pool and lake experiments further confirm the superior performance of the vertically arranged approach.

     

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