基于互谱测量拟合的传感器阵列增益相位校准
Gain and phase calibration of sensor arrays from ambient noise by cross-spectral measurements fitting
摘要：
我们解决的问题，盲增益和相位校准的传感器阵列从环境噪声。主要动机是通过避免复杂的程序设置来简化校准过程。我们表明，计算样本的协方差矩阵在扩散场是足够的恢复复杂的增益。为此，我们制定了一个非凸最小二乘问题的样本和模型协方差的基础上。我们提出用低秩矩阵近似的方法来求解，并相应地导出了两个有效的近似算法。第一个解决的问题修改与凸松弛，以确保解决方案是一个全球性的最小值，和第二个直接解决初始的非凸问题。根据不同的传感配置，我们研究了所提出的算法的效率的数值和实验结果。这些表明，有效的校准高度依赖于测量如何相关。也就是说，在场被空间过采样时更准确地实现估计。
英文摘要：
We address the problem of blind gain and phase calibration of a sensor array from ambient noise. The key motivation is to ease the calibration process by avoiding a complex procedure setup. We show that computing the sample covariance matrix in a diffuse field is sufficient to recover the complex gains. To do so, we formulate a non-convex least-square problem based on sample and model covariances. We propose to obtain a solution by low-rank matrix approximation, and two efficient proximal algorithms are derived accordingly. The first one solves the problem modified with a convex relaxation to guarantee that the solution is a global minimizer, and the second one directly solves the initial non-convex problem. We investigate the efficiency of the proposed algorithms by both numerical and experimental results according to different sensing configurations. These show that efficient calibration highly depends on how the measurements are correlated. That is, estimation is achieved more accurately when the field is spatially over-sampled.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
