摘要
Wireless indoor localization has attracted growing research interest in the mobile computing community for the last decade. Various available indoor signals, including radio frequency, ambient, visual, and motion signals, are extensively exploited for location estimation in indoor environments. The physical measurements of these signals, however, are still limited by both the resolution of devices and the spatial-temporal variability of the signals. One type of noisy signal complemented by another type of signal can benefit the wireless indoor localization in many ways, since these signals are related in their physics and independent in noise. In this article, we survey the new trend of integrating multiple chaotic signals to facilitate the creation of a crowdsourced localization system. Specifically, we first present a three-layer framework for crowdsourcing-based indoor localization by integrating multiple signals, and illustrate the basic methodology for making use of the available signals. Next, we study the mainstream signals involved in indoor localization approaches in terms of their characteristics and typical usages. Furthermore, considering multiple different outputs from different signals, we present significant insights to integrate them together, to achieve localizability in different sce- narios.
Wireless indoor localization has attracted growing research interest in the mobile computing community for the last decade. Various available indoor signals, including radio frequency, ambient, visual, and motion signals, are extensively exploited for location estimation in indoor environments. The physical measurements of these signals, however, are still limited by both the resolution of devices and the spatial-temporal variability of the signals. One type of noisy signal complemented by another type of signal can benefit the wireless indoor localization in many ways, since these signals are related in their physics and independent in noise. In this article, we survey the new trend of integrating multiple chaotic signals to facilitate the creation of a crowdsourced localization system. Specifically, we first present a three-layer framework for crowdsourcing-based indoor localization by integrating multiple signals, and illustrate the basic methodology for making use of the available signals. Next, we study the mainstream signals involved in indoor localization approaches in terms of their characteristics and typical usages. Furthermore, considering multiple different outputs from different signals, we present significant insights to integrate them together, to achieve localizability in different sce- narios.
基金
The authors would like to thank the anonymous reviewers for their valuable comments. This work was partly supported by the National Natural Science Foundation of China （Grant No. 61422214）, National Basic Research Program （973 program） （2014CB347800）, the Program for New Century Excellent Talents in University, the Hunan Provincial Natural Science Fund for Distinguished Young Scholars （2016JJ1002）, and the Research Funding of NUDT （JQ14-05-02 and ZDYYJCYJ20140601）.
