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中文核心期刊

存在阵元姿态误差的声矢量圆阵特征结构方位估计

Eigenstructure-based DOA estimation for circular acoustic vector sensor array with element attitude errors

  • 摘要: 为提高声矢量圆阵存在阵元姿态误差时的方位估计性能, 基于幅度加权处理后协方差矩阵的特征值分解, 提出一种特征结构方位估计方法。在各向同性噪声场下, 通过对声矢量圆阵进行幅度加权处理以减小声压通道和振速通道噪声功率不一致性影响; 根据实际导向向量和噪声子空间的正交性构建目标函数, 并在预设方位角下基于此目标函数建立关于姿态误差的最优化问题, 求解得到包含姿态误差参数的向量闭式解使得目标函数最小化以估计信源方位角; 并利用联合迭代的方式进一步提高方位估计精度。仿真结果表明, 所提方法在声矢量圆阵存在阵元姿态误差时具有较高的稳健性, 获得了良好的方位估计性能和较高的方位角分辨能力; 外场实验数据验证了所提方法的有效性。

     

    Abstract: To enhance the direction of arrival (DOA) estimation performance of a circular acoustic vector array (CAVA) with element attitude errors, this paper proposes eigenstructure-based DOA estimation methods utilizing the eigendecomposition of the amplitude-weighted covariance matrix. In the scenario of isotropic ambient noise, the amplitude weighting processing is applied to the CAVA to reduce the impact of the inequality between the noise powers of the pressure and velocity channels. An objective function is constructed based on the orthogonality between the actual steering vector and the noise subspace. Using this objective function, the optimization problem concerning attitude errors is established for a preset azimuth. The closed-form solution of the vector containing the attitude error parameter is then solved to minimize the objective function, thereby enabling the estimation of the source DOAs. Furthermore, the accuracy of DOA estimation can be further improved through joint iterative processing. The simulation results demonstrate that the proposed methods exhibit strong robustness in the presence of element attitude errors, and obtain better DOA estimation performance and higher azimuth resolution. The experimental results further verify the effectiveness of the proposed methods.

     

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