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III: Small: Indoor Spatial Query Evaluation and Trajectory Tracking with Bayesian Filtering Techniques

III：小：使用贝叶斯过滤技术的室内空间查询评估和轨迹跟踪

基本信息

批准号：
1618669
负责人：
Xiao Qin
金额：
$ 50万
依托单位：
Auburn University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618669&HistoricalAwards=false
关键词：
III Small Indoor Spatial Query

项目摘要

Today most people spend a significant portion of their time daily in indoor spaces such as office buildings, shopping malls, convention centers, subway systems, and many other structures. In addition, indoor spaces are becoming increasingly large and complex. For instance, the New York City Subway has 469 stations and contains 233 miles of routes. In 2014, the subway system delivered over 1.75 billion rides, averaging approximately 5.6 million daily rides on weekdays. Therefore, users will have more and more demand for launching location-based (spatial) queries for finding friends, objects, or points of interest in indoor spaces. However, existing spatial query evaluation techniques for outdoor environments cannot be applied in indoor spaces because these techniques assume that user locations can be acquired from GPS signals or cellular positioning, but the assumption does not hold in covered indoor spaces. Furthermore, indoor spaces are usually modeled differently from outdoor spaces. In indoor environments, user movements are enabled or constrained by entities and topologies such as doors, walls, and hallways. Radio Frequency Identification (RFID) is a very popular electronic tagging technology that allows objects to be automatically identified at a distance using an electromagnetic challenge-and-response exchange of data. An RFID-based system consists of a large number of low-cost tags that are attached to objects and readers, which can identify tags without a direct line-of-sight through RF communications. RFID technologies have become increasingly popular over the last decade with applications in areas such as supply chain management, health care, and transportation. In this project, the researchers consider the setting of an indoor environment where a number of RFID readers are deployed in the indoor space. Each user is associated with an RFID tag, which can be identified by a reader when the user is within the detection range of the reader. Given the history of RFID raw readings from all the readers, the research team is in the position to design a system that can efficiently answer indoor spatial queries and track trajectories of objects. The research results of this project will improve the performance of numerous high value-added indoor applications and hence benefit the economy of our country. In addition, the ability to be able to locate people in indoor spaces will improve emergency response. The project will promote teaching, learning, and training by exposing both undergraduate and graduate students to mathematical and technological underpinnings in the field of spatial data management.In this project, the research team will develop an array of techniques to derive accurate object locations from erroneous RFID raw readings for supporting indoor spatial query evaluation and trajectory tracking. With accurate spatial query results and trajectory information, many high level applications (e.g., indoor layout planning and indoor location-based services) can be supported. This project will contribute to the research community by piloting novel indoor data management techniques that will accomplish the following goals: (1) develop and compare a number of Bayesian filtering-based location inference methods for evaluating spatial queries in indoor environments, (2) design novel indoor query evaluation algorithms for various spatial query types such as range query and k nearest neighbor query, (3) invent a hidden Markov model-based approach for indoor object trajectory tracking, and (4) implement a simulation toolkit and a prototype system, where all the components will be integrated for performance evaluation. All the research results and publications will be available on the project web site (http://www.eng.auburn.edu/~xqin/Indoor.htm).

如今，大多数人每天花费很大一部分时间在室内空间，例如办公楼、购物中心、会议中心、地铁系统和许多其他建筑。此外，室内空间变得越来越大和复杂。例如，纽约地铁有 469 个车站，线路总长 233 英里。 2014年，地铁系统的乘客量超过17.5亿人次，工作日日均乘客量约560万人次。因此，用户将越来越需要启动基于位置（空间）的查询来查找室内空间中的朋友、物体或兴趣点。然而，现有的室外环境空间查询评估技术无法应用于室内空间，因为这些技术假设可以从GPS信号或蜂窝定位获取用户位置，但该假设在有覆盖的室内空间中并不成立。此外，室内空间的建模通常与室外空间不同。在室内环境中，用户的移动由实体和拓扑（例如门、墙壁和走廊）启用或限制。射频识别 (RFID) 是一种非常流行的电子标签技术，它允许使用电磁质询和响应数据交换来自动识别远距离的物体。基于RFID的系统由大量附着在物体和阅读器上的低成本标签组成，它们可以通过射频通信在没有直接视线的情况下识别标签。 RFID 技术在过去十年中变得越来越流行，应用于供应链管理、医疗保健和运输等领域。在这个项目中，研究人员考虑了室内环境的设置，在室内空间中部署了许多 RFID 阅读器。每个用户都与一个 RFID 标签相关联，当用户位于阅读器的检测范围内时，该标签可以被阅读器识别。考虑到所有阅读器的 RFID 原始读数历史，研究团队能够设计一个能够有效回答室内空间查询并跟踪物体轨迹的系统。该项目的研究成果将提高众多高附加值室内应用的性能，从而造福我国的经济。此外，能够在室内空间定位人员的能力将改善应急响应。该项目将通过让本科生和研究生接触空间数据管理领域的数学和技术基础来促进教学、学习和培训。在该项目中，研究团队将开发一系列技术，从错误的 RFID 原始读数中得出准确的物体位置，以支持室内空间查询评估和轨迹跟踪。凭借准确的空间查询结果和轨迹信息，可以支持许多高级应用（例如室内布局规划和室内基于位置的服务）。该项目将通过试验新颖的室内数据管理技术为研究界做出贡献，这些技术将实现以下目标：(1) 开发和比较多种基于贝叶斯过滤的位置推断方法，用于评估室内环境中的空间查询，(2) 为各种空间查询类型（例如范围查询和 k 最近邻查询）设计新颖的室内查询评估算法，(3) 发明一种基于隐马尔可夫模型的方法室内物体轨迹跟踪，(4) 实施模拟工具包和原型系统，其中所有组件将集成以进行性能评估。所有研究成果和出版物将在项目网站（http://www.eng.auburn.edu/~xqin/Indoor.htm）上提供。