Effective sampling of received signal strength for location estimation

最近，人们对基于位置的应用的设计和开发产生了很大的兴趣。这些应用的例子可以在Google Latitude、Apple基于位置的服务以及诸如Skyhook、Ekahau、PointInside、Micello等公司的活动中找到。基于位置的服务的主要限制是GPS服务在室内不可用，因此仅提供室外应用。 该提案的主要研究者（PI）的研究小组在过去两年中一直活跃在室内定位领域。该小组设计并开发了一种基于WiFi接入点接收信号强度（RSS）的新型定位器。该系统目前正在加拿大国家盲人研究所（CNIB）接受视力障碍者的测试，作为室内导航系统，其演示可在PI的网站上获得。我们的实验与约20名受试者有显着的视觉障碍表明，开发的原型可以成功地用于定位个人，并帮助他们在室内导航（CNIB在我们的测试）。 像任何其他基于RSS的位置估计器一样，我们的系统需要一个训练阶段，在这个阶段中，WiFi信号的强度被测量并存储在一个数据库中，称为无线电地图。在过去十年中一直困扰着信号处理界的一个主要挑战是生成和维护一个强大的最新无线电地图。我们相信，我们可以提供一个解决这个长期悬而未决的问题，这将彻底改变室内位置估计。我们的出发点是一个新的稀疏建模的位置估计问题，使我们能够使用最近开发的理论下的概念压缩感知（CS）

Recently there has been much interest in the design and development of location based applications. Examples of such applications can be found in Google Latitude, Apple Location Based Services, and the activities of companies such as Skyhook, Ekahau, PointInside, Micello, and so on. A major limitation in location-based services is that the GPS service is not available indoors and hence only outdoor applications are offered. The research group of the Principal Investigator (PI) of this proposal has been active in the area of indoor localization over the last two years. The group has designed and developed a novel localizer based on the received signal strength (RSS) of WiFi access points. The system is currently being tested by individuals with visual impairment at the Canadian National Institute for Blind (CNIB) as an in indoor navigation system, a demo of which is available at PI's website. Our experiments with about twenty subjects with significant visual impairment show that the developed prototype can be used successfully to locate individuals and to help them navigate indoors (CNIB in our tests). Like any other RSS-based location estimator, our system requires a training phase in which the strength of the WiFi signal is measured and stored in a database, called the radio map. A main challenge, which has been haunting the signal processing community over the last decade, has been to generate and maintain a robust up-to-date radio map. We believe that we can provide a solution to this long outstanding problem, which will revolutionize the indoor location estimation. Our point of departure is a novel sparse modelling of the location estimation problem that allows us to use the recently developed theory under the concept of compressive sensing (CS)