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LocateMe: Magnetic-Fields-Based Indoor Localization Using Smartphones

机译:LocateMe:使用智能手机的基于磁场的室内定位

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Fine-grained localization is extremely important to accurately locate a user indoors. Although innovative solutions have already been proposed, there is no solution that is universally accepted, easily implemented, user centric, and, most importantly, works in the absence of GSM coverage or WiFi availability. The advent of sensor rich smartphones has paved a way to develop a solution that can cater to these requirements. By employing a smartphone's built-in magnetic field sensor, magnetic signatures were collected inside buildings. These signatures displayed a uniqueness in their patterns due to the presence of different kinds of pillars, doors, elevators, etc., that consist of ferromagnetic materials like steel or iron. We theoretically analyze the cause of this uniqueness and then present an indoor localization solution by classifying signatures based on their patterns. However, to account for user walking speed variations so as to provide an application usable to a variety of users, we follow a dynamic time-warping-based approach that is known to work on similar signals irrespective of their variations in the time axis. Our approach resulted in localization distances of approximately 2m-6m with accuracies between 80-100% implying that it is sufficient to walk short distances across hallways to be located by the smartphone. The implementation of the application on different smartphones yielded response times of less than five secs, thereby validating the feasibility of our approach and making it a viable solution.
机译:细粒度的定位对于在室内准确定位用户非常重要。尽管已经提出了创新的解决方案,但是没有一种解决方案被普遍接受,易于实施,以用户为中心,并且最重要的是,在没有GSM覆盖范围或WiFi可用性的情况下仍然可以工作。传感器丰富的智能手机的出现为开发一种可满足这些要求的解决方案铺平了道路。通过使用智能手机的内置磁场传感器,可以在建筑物内部收集磁签名。由于存在由钢或铁等铁磁材料组成的各种柱子,门,电梯等,这些签名在样式上显示出独特性。我们从理论上分析了这种唯一性的原因,然后通过基于签名的模式对签名进行分类,提出了一种室内定位解决方案。但是,为了考虑用户步行速度的变化以便提供可用于各种用户的应用程序,我们遵循一种基于动态时间扭曲的方法,该方法已知适用于相似的信号,而不管它们在时间轴上的变化如何。我们的方法得出的定位距离约为2m-6m,准确度在80%至100%之间,这意味着通过智能手机定位走过走廊的短距离就足够了。在不同的智能手机上实施该应用程序后,响应时间不到5秒,从而验证了我们方法的可行性并使其成为可行的解决方案。

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