Effective sampling of received signal strength for location estimation

点击翻译按钮获取中文摘要

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)