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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).

今天，大多数人每天花很大一部分时间在室内空间，如办公楼、购物中心、会议中心、地铁系统和许多其他结构中。此外，室内空间也变得越来越大和复杂。例如，纽约市的地铁有469个车站，总长233英里。2014年，地铁系统运送乘客超过17.5亿人次，工作日平均每天约560万人次。因此，用户将有越来越多的需求来发起基于位置（空间）的查询，以寻找朋友、对象或室内空间中的兴趣点。然而，现有的室外环境空间查询评估技术不能应用于室内空间，因为这些技术假设用户位置可以从GPS信号或蜂窝定位中获得，但在室内空间不适用。此外，室内空间的模型通常与室外空间不同。在室内环境中，用户的移动受到实体和拓扑（如门、墙和走廊）的支持或约束。射频识别（RFID）是一种非常流行的电子标签技术，它允许使用电磁挑战-响应数据交换在一定距离内自动识别对象。基于rfid的系统由大量低成本的标签组成，这些标签附着在物体和读取器上，通过射频通信可以在没有直接视线的情况下识别标签。RFID技术在过去十年中越来越受欢迎，在供应链管理、医疗保健和运输等领域得到了应用。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读取器。每个用户都与一个RFID标签相关联，当用户在阅读器的检测范围内时，阅读器可以识别该标签。考虑到所有读取器的RFID原始读数的历史，研究团队可以设计一个系统，可以有效地回答室内空间查询和跟踪物体的轨迹。该项目的研究成果将提高众多高附加值室内应用的性能，从而有利于我国的经济发展。此外，能够在室内空间定位人员的能力将改善应急反应。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，促进教学、学习和培训。在这个项目中，研究团队将开发一系列技术，从错误的RFID原始读数中获得准确的目标位置，以支持室内空间查询评估和轨迹跟踪。通过精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位置服务）。该项目将通过试点新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)开发并比较多种基于贝叶斯滤波的室内空间查询评估方法；(2)设计新的室内查询评估算法，用于各种空间查询类型，如距离查询和k近邻查询；(3)发明一种基于隐马尔可夫模型的室内目标轨迹跟踪方法；(4)实现仿真工具包和原型系统，其中所有组件将集成在一起进行性能评估。所有的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。