NeTS: Small: Integrating Electronic and Visual Signals for Accurate Localization

NeTS：小型：集成电子和视觉信号以实现精确定位

• 批准号: 1218876
• 负责人: Dong Xuan
• 金额: $ 43万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218876&HistoricalAwards=false
• 关键词: NeTS; Small; Integrating; Electronic; Visual

Nowadays, an increasing number of objects can be represented by their wireless electronic identifiers. For examples, people can be recognized by their cellular phones' or laptops' MAC addresses and products can be identified by their RFID numbers. Localizing objects with electronic identifiers is more and more important as our society is becoming increasingly "digitalized". However, traditional wireless localization techniques cannot meet the rapidly mounting requirements of accurate and cost-effective localization. Some of them need expensive hardware to achieve high accuracy, which is impractical for massive deployments, while others such as WiFi RSSI based localization are inaccurate and not robust to environmental noise. This project designs a novel localization methodology called EVLoc. In EVLoc, visual signals (V signals) are used to improve the accuracy of electronic localization, as the accuracy of visual localization is around 0.1-0.5 meter. This technique fully leverages V signals' high accuracy and the pervasiveness of electronic signals (E signals). The project conducts three main research tasks: (1) perform object localization given complete and distinct E and V signals, (2) perform object localization in practical settings when the E and V signals are not complete and distinct, and (3) implement the E-V approach to validate its accuracy and efficacy. This pioneering research develops a transformative approach that integrates E and V signals for accurate localization. The approach can be applied to many emerging applications such as epidemic control and tracking of elderly people, children, and those exhibiting at-risk behaviors for healthcare purposes.

如今，越来越多的物体可以通过它们的无线电子标识符来表示。例如，人们可以通过他们的手机或笔记本电脑的MAC地址来识别，产品可以通过他们的RFID号码来识别。随着我们的社会越来越“数字化”，用电子标识来定位物体变得越来越重要。然而，传统的无线定位技术已不能满足快速增长的精确、经济的定位要求。其中一些需要昂贵的硬件来实现高精度，这对于大规模部署是不切实际的，而其他的，如基于WiFi RSSI的定位是不准确的，并且对环境噪声的鲁棒性不强。本项目设计了一种名为EVLoc的新颖定位方法。在EVLoc中，使用视觉信号（V信号）来提高电子定位的精度，因为视觉定位的精度在0.1-0.5米左右。该技术充分利用了V信号的高精度和电子信号（E信号）的普遍性。本项目主要开展三项研究任务：(1)在完整且清晰的E、V信号条件下进行目标定位；(2)在E、V信号不完整且不清晰的情况下进行实际场景下的目标定位；(3)实施E-V方法验证其准确性和有效性。这项开创性的研究开发了一种变革性的方法，集成了E和V信号，以实现准确的定位。该方法可以应用于许多新兴应用，例如流行病控制和跟踪老年人、儿童以及出于医疗目的表现出危险行为的人。