Localization of Wireless Terminals via Deep Learning

无线终端的本地化在过去几年中获得了发展势头。现在正在开发许多提供基于位置的服务的应用程序。谷歌地图、Find My iPhone、Virtual Reality耳机和poksammon Go是使用位置信息的应用程序的一些示例。随着5G、物联网、智慧城市等技术的普及，将开发出各种新应用。不幸的是，GPS服务无法在室内使用，或者在主要城市的市中心等被高层建筑遮挡的地区非常不准确。应该开发新技术来补充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位置。我们已经开发了新的定位技术，基于接收信号强度（RSS）使用先进的信号处理方法，如压缩感知。我们研究了基于定位和跟踪的WiFi接收信号强度，通过无监督学习和众包的设备多样性。我们有两项专利技术和一项正在申请中的专利。目前，我们正在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以前研究的延续和改进。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，在报道的文献中有一些缺点，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2.4 GHz和5.8 GHz的ISM全频段，且范围有限。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势头强劲。深度神经网络可以提取广泛的复杂特征，这些特征可以用于回归和分类。我们的目标是开发有效的定位方法，可以在没有任何专门硬件的情况下，在现成的手机上以分米级的精度定位用户。**

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. **