标题： 在无位置感知的移动传感设置中学习随机空间场的分布 显示英文标题

标题： On Learning the Distribution of a Random Spatial Field in a Location-Unaware Mobile Sensing Setup

摘要： 在环境监测和污染控制等应用中，物理量由时空场进行建模。 了解这些场的统计分布作为空间、时间或两者的函数是有意义的。 在本工作中，我们的目标是学习固定一维路径上的时空场的统计分布，作为空间位置的函数，在没有位置信息的情况下。 使用静态传感器网络进行的空间场分析是文献中研究得较为透彻的问题。 最近，由于设置空间采样密度的灵活性和较低的硬件成本，以及更大的空间覆盖范围，移动传感器被用于此目的。 使用移动传感器的主要挑战是其位置不确定性。 获取样本的位置信息需要额外的硬件和成本。 因此，我们考虑一种情况，即使用一个简单的移动传感设备沿固定长度路径对时空场进行采样，在遍历路径时记录场值而不提供任何位置信息。 我们询问在对场的一些简单假设下，是否可以利用无位置信息的移动传感器的样本学习场的统计分布作为空间位置的函数。 我们对此问题给出了肯定的回答，并提供了系列分析和实验结果来支持我们的主张。 显示英文摘要

摘要： In applications like environment monitoring and pollution control, physical quantities are modeled by spatio-temporal fields. It is of interest to learn the statistical distribution of such fields as a function of space, time or both. In this work, our aim is to learn the statistical distribution of a spatio-temporal field along a fixed one dimensional path, as a function of spatial location, in the absence of location information. Spatial field analysis, commonly done using static sensor networks is a well studied problem in literature. Recently, due to flexibility in setting the spatial sampling density and low hardware cost, owing to larger spatial coverage, mobile sensors are used for this purpose. The main challenge in using mobile sensors is their location uncertainty. Obtaining location information of samples requires additional hardware and cost. So, we consider the case when the spatio-temporal field along the fixed length path is sampled using a simple mobile sensing device that records field values while traversing the path without any location information. We ask whether it is possible to learn the statistical distribution of the field, as a function of spatial location, using samples from the location-unaware mobile sensor under some simple assumptions on the field. We answer this question in affirmative and provide a series of analytical and experimental results to support our claim.