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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682530
• 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. **

在过去的几年中，无线终端的局部化已经增长。现在正在开发许多提供基于位置服务的应用程序。 Google Map，查找我的iPhone，虚拟现实耳机和PokémonGO是使用位置信息的应用程序的一些示例。当5G，Things-of-Things和Smart Cities Technologies提供时，将开发各种新应用程序。不幸的是，GPS服务在狗中不可用，或者在被主要城市的市区核心等高层建筑所阻挡的地区非常不准确。应开发新技术以补充GP并普遍提供位置估计。 *****我们在多伦多大学的研究一直在最近的发现。我们使用预先信号处理方法（例如压缩传感）基于接收的信号强度（RSS）开发了新的定位技术。我们研究了WiFi通过无监督学习和众包收到了基于信号强度的本地化和跟踪，设备多样性。我们有两种专利技术和尚待专利申请。目前，我们正在通过图像和视频信号处理进行位置查找。 *****拟议的研究活动将是我们以前的研究的延续和完善。新的研究表明，使用WIFI信号的通道状态信息（CSI）可以带来显着的位置准确性，并且单个访问点足以具有决策水平的定位准确性。但是，报告的文献中存在一些缺点，这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2.4 GHz和5.8 GHz的整个ISM频段，其范围是有限的。我们的工作将回答CSI应用中的一些开放问题。我们将使用深度学习方法进行RSS和CSI的位置查找。深度学习在解决难题，例如语音和视频分类，大数据和医学成像等困难问题方面已有势头。深度神经元网络可以提取可用于回归和分类的广泛复杂特征。我们的目标是开发有效的位置查找方法，这些方法可以在分解器级别上定位用户在现成的手机上精确操作而无需任何专业硬件。 **