论文标题
分布式系统中目标定位的半参数模型
A semi-parametric model for target localization in distributed systems
论文作者
论文摘要
分布式系统是一个关键的技术基础架构,用于监视跨时空的不同系统。其广泛应用的示例包括:精确农业,监视,生态系统和物理基础设施监测,动物行为和跟踪,灾难响应和恢复以等。这样的系统包括大量在固定位置的传感器设备,其中每个单独的传感器获得了随后在中央处理节点上融合和处理的测量值。此类系统的一个关键问题是检测目标并确定其位置,为此,已经开发出大量文献的重点是采用参数模型来从目标到设备进行信号衰减。在本文中,我们采用了一种非参数方法,该方法仅假定信号作为传感器和目标之间距离的函数的函数。我们为目标位置提出了一个简单的调整参数估计器,即简单的分数估计器(SSCE)。我们表明,SSCE是$ \ sqrt {n} $一致的,并且具有高斯极限分布,可用于为目标位置构建渐近置信区域。我们通过广泛的模拟研究了SSCE的性能,并最终在视频监视数据集中展示了目标检测的应用。
Distributed systems serve as a key technological infrastructure for monitoring diverse systems across space and time. Examples of their widespread applications include: precision agriculture, surveillance, ecosystem and physical infrastructure monitoring, animal behavior and tracking, disaster response and recovery to name a few. Such systems comprise of a large number of sensor devices at fixed locations, wherein each individual sensor obtains measurements that are subsequently fused and processed at a central processing node. A key problem for such systems is to detect targets and identify their locations, for which a large body of literature has been developed focusing primarily on employing parametric models for signal attenuation from target to device. In this paper, we adopt a nonparametric approach that only assumes that the signal is nonincreasing as function of the distance between the sensor and the target. We propose a simple tuning parameter free estimator for the target location, namely, the simple score estimator (SSCE). We show that the SSCE is $\sqrt{n}$ consistent and has a Gaussian limit distribution which can be used to construct asymptotic confidence regions for the location of the target. We study the performance of the SSCE through extensive simulations, and finally demonstrate an application to target detection in a video surveillance data set.