论文标题
通过跨光谱测量拟合从环境噪声中的传感器阵列的增益和相位校准
Gain and phase calibration of sensor arrays from ambient noise by cross-spectral measurements fitting
论文作者
论文摘要
我们解决了来自环境噪声的传感器阵列的盲收益和相位校准的问题。关键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，计算扩散场中的样品协方差矩阵足以恢复复杂的收益。为此，我们基于样本和模型协方差制定了一个非凸照最小二乘问题。我们建议通过低级矩阵近似获得溶液，并因此得出了两种有效的近端算法。第一个解决了通过凸松弛修改的问题，以确保解决方案是全局最小化器，而第二个则直接解决了初始的非凸问题。我们根据不同的感应构型通过数值和实验结果研究了所提出的算法的效率。这些表明，高效校准高度取决于测量的相关性。也就是说，当磁场被空间过度采样时，可以更准确地实现估计。
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.
