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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=667436
• 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. **

无线终端的本地化在过去几年中获得了动力。现在正在开发许多提供基于位置的服务的应用。Google地图、Find My iPhone、Virtual Reality Headsets和Pokémon Go是使用位置信息的应用程序的一些示例。当5G、物联网和智慧城市技术可用时，将开发各种新的应用。不幸的是，GPS服务在室内不可用，或者在被高层建筑阻挡的区域（如主要城市的市中心）非常不准确。应开发新技术，以补充全球定位系统，并提供无处不在的位置估计。** 我们在多伦多大学的研究一直在寻找位置在最近的过去。我们开发了基于接收信号强度（RSS）的新定位技术，使用先进的信号处理方法，如压缩感知。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和众包。我们拥有两项专利技术和一项正在申请的专利。目前，我们正在通过图像和视频信号处理进行定位。* *新的研究表明，使用WiFi信号的信道状态信息（CSI）可以显著提高定位精度，并且单个接入点足以具有分米级的定位精度。然而，有一些缺点，在文献报道，限制了这些方法在实践中的应用。该方法只适用于一对节点，使用2.4 GHz和5.8 GHz的整个ISM频段，范围有限。我们的工作将回答一些开放的问题，在CSI的应用。我们将使用深度学习方法在RSS和CSI上进行定位。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进展。深度神经网络可以提取广泛的复杂特征，可用于回归和分类。我们的目标是开发有效的定位方法，可以定位用户在分米级的精度操作现成的电话，没有任何专门的硬件。 **