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.
翻译:分布式系统是监测不同空间和时间系统的关键技术基础设施,其广泛应用的例子包括:精确农业、监视、生态系统和有形基础设施监测、动物行为和跟踪、灾害应对和复原等。这些系统由固定地点的大量传感器装置组成,每个传感器在固定地点获得测量结果,然后在中央处理节点进行整合和处理。这些系统的一个关键问题是检测目标并确定其位置,为此开发了大量文献,主要侧重于使用参数模型从目标到装置的信号衰减。在本文件中,我们采取非参数方法,仅假设该信号在传感器和目标之间的距离功能上没有增加。我们建议为目标地点提供一个简单的调整参数,即简单的分数估计器(SSCE)。我们显示,SSCE是美元,它具有一致性,并且有高斯极限分布,可用于构建目标位置的信号防御性信任区。我们研究了通过广泛模拟检测和检测目标的性数据应用情况。我们研究了通过图像检测到目标位置的检测结果。