论文标题
关于偏移校准中参考的选择
On the choice of reference in offset calibration
论文作者
论文摘要
传感器校准是任何网络网络物理系统中必不可少的任务。在本文中，我们考虑了一个受偏移误差困扰的传感器网络，测量了等级-1信号子空间，每个传感器都会在带有添加剂零均值高斯噪声的线性模型下收集测量值。在对基础噪声协方差的不同假设下，我们研究了使用任意参考来估计传感器偏移的效果，与“所有未知偏移”的平均值作为参考相反。我们首先表明\ emph {平均参考引用会产生有效的最小方差无偏估计器。如果基本噪声本质上是同质的，则与网络中的任何任意选择的参考相比，我们证明\ emph {peraver {平均值{平均值会产生$ 2 $的提高。此外，当基础噪声是独立但不完全相同时，我们得出了\ emph {平均}参考提供的改进的表达式。我们使用传感器网络中时钟同步的问题来证明我们的结果，并讨论未来工作的方向。
Sensor calibration is an indispensable task in any networked cyberphysical system. In this paper, we consider a sensor network plagued with offset errors, measuring a rank-1 signal subspace, where each sensor collects measurements under a linear model with additive zero-mean Gaussian noise. Under varying assumptions on the underlying noise covariance, we investigate the effect of using an arbitrary reference for estimating the sensor offsets, in contrast to the `average of all the unknown offsets' as a reference. We first show that the \emph{average} reference yields an efficient minimum variance unbiased estimator. If the underlying noise is homoscedastic in nature, then we prove the \emph{average} reference yields a factor $2$ improvement on the variance, as compared to any arbitrarily chosen reference within the network. Furthermore, when the underlying noise is independent but not identical, we derive an expression for the improvement offered by the \emph{average} reference. We demonstrate our results using the problem of clock synchronization in sensor networks, and discuss directions for future work.
