Indoor localization is an emerging application domain that promises to enhance the way we navigate in various indoor environments, as well as track equipment and people. Wireless signal-based fingerprinting is one of the leading approaches for indoor localization. Using ubiquitous Wi-Fi access points and Wi-Fi transceivers in smartphones has enabled the possibility of fingerprinting-based localization techniques that are scalable and low-cost. But the variety of Wi-Fi hardware modules and software stacks used in today's smartphones introduce errors when using Wi-Fi based fingerprinting approaches across devices, which reduces localization accuracy. We propose a framework called SHERPA-HMM that enables efficient porting of indoor localization techniques across mobile devices, to maximize accuracy. An in-depth analysis of our framework shows that it can deliver up to 8× more accurate results as compared to state-of-the-art localization techniques for a variety of environments.

室内本地化是一个新兴的应用领域，有望增强我们在各种室内环境中导航以及跟踪设备和人员的方式。基于无线信号的指纹识别是室内定位的主要方法之一。在智能手机中使用无处不在的Wi-Fi接入点和Wi-Fi收发器已使基于指纹的本地化技术成为可能，这些技术具有可扩展性和低成本。但是，当今的智能手机中使用的各种Wi-Fi硬件模块和软件堆栈会在跨设备使用基于Wi-Fi的指纹识别方法时引入错误，从而降低了定位精度。我们提出了一个名为SHERPA-HMM的框架支持跨移动设备高效移植室内定位技术，以最大程度地提高准确性。对我们框架的深入分析表明，与针对各种环境的最新本地化技术相比，它可以提供多达8倍的准确结果。