To improve the performance of direction of arrival (DOA) estimation under the coexistence of nonuniform noise and axial angle bias, we propose new DOA estimation methods based on the eigendecomposition of a covariance matrix constructed by the analytic velocity and acoustic pressure. The analytical velocity model can convert axial angle bias into phase error to facilitate its estimation. Meanwhile, the diagonal unloading (DU) technique equalizes the noise power to minimize the effect of noise non-uniformity, and the minimum diagonal element of this covariance matrix can precisely determine the amount of DU. Next, two eigenstructure-based estimation algorithms are developed from the objective function formulated by the orthogonality of the true steering vector and the noise subspace. The first method is to get a quadratic matrix from this objective function and estimate the DOAs by judging its singularity at different spatial angles. In the second method, an optimization problem is formulated to deduce a closed-form solution of the bias weight vector corresponding to the preset spatial angle, and a joint iterative approach further improves the estimation accuracy of the DOAs and axial angle bias parameters. Simulation results are provided to show the superiority of the proposed methods.

中文翻译：

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非均匀噪声中轴向偏角圆形声矢量传感器阵列DOA估计的特征结构方法

为了提高非均匀噪声和轴向角偏差共存下的到达方向（DOA）估计的性能，我们提出了基于由解析速度和声压构造的协方差矩阵的特征分解的新的DOA估计方法。解析速度模型可以将轴向角偏差转换为相位误差，以方便其估计。同时，对角卸载（DU）技术均衡噪声功率以最小化噪声不均匀性的影响，并且该协方差矩阵的最小对角元素可以精确确定DU的量。接下来，根据由真实引导矢量和噪声子空间的正交性制定的目标函数，开发了两种基于特征结构的估计算法。第一种方法是由该目标函数得到二次矩阵，通过判断其在不同空间角度的奇异性来估计DOA。第二种方法通过优化问题推导出预设空间角度对应的偏置权重向量的闭式解，并采用联合迭代的方法进一步提高了DOA和轴角偏置参数的估计精度。仿真结果表明了所提出方法的优越性。