Localization of Wireless Terminals via Deep Learning

通过深度学习定位无线终端

• 批准号: RGPIN-2017-06625
• 负责人: Valaee, Shahrokh
• 金额: $ 2.04万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712284
• 关键词: Localization; Wireless; Terminals; via; Deep

Localization of wireless terminals has gained momentum over the last few years. Many applications are now being developed that provide location based services. Google map, Find My iPhone, Virtual Reality Headsets, and Pokémon Go are some examples of applications that use location information. Various new applications will be developed when 5G, Internet-of-Things, and Smart Cities technologies become available. Unfortunately, the GPS service is not available in indoors, or is very inaccurate in areas blocked by tall buildings such as downtown cores in major cities. New technologies should be developed to complement GPS and provide location estimation ubiquitously. Our research at the University of Toronto has been on location finding in recent past. We have developed new localization technologies based on received signal strength (RSS) using advance signal processing methods such as Compressive Sensing. We have studied WiFi received signal strength based localizations and tracking, device diversity through unsupervised learning, and crowdsourcing. We have two patented technologies and a pending patent application. Currently, we are working on location finding via image and video signal processing. The proposed research activity will be the continuation and refinement of our previous research. New studies have shown that using channel state information (CSI) of WiFi signals can give a significant gain in location accuracy, and that a single access point is sufficient to have decimeter level localization accuracy. There are, however, a few shortcomings in the reported literature that limits the application of these methods in practice. The method is only applicable to a pair of nodes, uses the whole ISM band in 2.4 GHz and 5.8 GHz, and its range is limited. Our work will answer some of the open problems in the application of CSI. We will use Deep Learning methods for location finding on RSS and CSI. Deep learning has gained momentum in tackling difficult problems such as voice and video classification, big data, and medical imaging. A deep neural network can extract a wide range of complex features that can be used in regression and classification. Our goal is to develop effective location finding methods that can locate users at decimeter level accuracy operating on off-the-shelf phones without any specialized hardware.

过去几年，无线终端的本地化势头强劲。现在正在开发许多提供基于位置的服务的应用程序。 Google 地图、查找我的 iPhone、虚拟现实耳机和 Pokémon Go 是使用位置信息的应用程序的一些示例。当5G、物联网、智慧城市等技术出现时，各种新的应用将会被开发出来。遗憾的是，GPS 服务在室内无法使用，或者在大城市市中心等高楼遮挡的区域非常不准确。应开发新技术来补充 GPS 并提供无处不在的位置估计。 我们在多伦多大学的研究最近一直在寻找地点。我们使用压缩感知等先进信号处理方法开发了基于接收信号强度 (RSS) 的新定位技术。我们研究了基于 WiFi 接收信号强度的定位和跟踪、通过无监督学习的设备多样性以及众包。我们拥有两项专利技术和一项正在申请的专利。目前，我们正在致力于通过图像和视频信号处理进行定位。 拟议的研究活动将是我们先前研究的延续和完善。新研究表明，使用 WiFi 信号的信道状态信息 (CSI) 可以显着提高定位精度，并且单个接入点足以实现分米级定位精度。然而，报道的文献中存在一些缺点限制了这些方法在实践中的应用。该方法仅适用于一对节点，使用2.4GHz和5.8GHz整个ISM频段，范围有限。我们的工作将回答 ​​CSI 应用中的一些开放性问题。我们将使用深度学习方法在 RSS 和 CSI 上进行位置查找。深度学习在解决语音和视频分类、大数据和医学成像等难题方面取得了长足发展。深度神经网络可以提取各种可用于回归和分类的复杂特征。我们的目标是开发有效的定位方法，无需任何专用硬件即可在现成手机上以分米级精度定位用户。