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首页> 外文会议>International Wireless Communications and Mobile Computing Conference >Indoor Localization on Smartphone Using PDR and Sparse Deployed BLE Beacons in Large Open Area
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Indoor Localization on Smartphone Using PDR and Sparse Deployed BLE Beacons in Large Open Area

机译：使用PDR和稀疏部署的BLE BLE在大型开放区域的室内定位

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摘要

A lot of work has demonstrated that satisfactory localization accuracy can be achieved in a typical office environment (e.g. teaching building, hospital and office building) which contains many small rooms on both sides of narrow corridors. However, it is observed that the accuracy degrades a lot when it comes to the large open environment, such as halls, supermarkets and airports. Another problem is the higher hardware cost and computational burden caused by the large localization area. In this paper, we analyze the feasibility of each technique in large open environment and then propose a practical indoor positioning system, in which PDR cumulative error is intermittently reduced by sparsely deployed BLE beacon. One of our contributions is to propose a single-beacon-based positioning method that is based on the RSS peak and user's walking direction. Furthermore, we adopt a robust RSS peak detection method to recognize the real RSS peak accurately. Meanwhile, to compensate for the delay in RSS peak detection, PDR is used to infer the corresponding displacement. Finally, unlike the traditional step length estimation methods establishing an empirical formula, we propose a novel parameters-free method which doesn't require offline training and is feasible for everyone. Experimental results show that our proposed system exhibits 90% accuracy within 3.5m for a total distance traveled of 377m in the large open environment.
机译：大量工作表明，在典型的办公环境（例如教学建筑，医院和办公楼）中可以实现令人满意的定位准确性，其中包含狭窄走廊两侧的许多小房间。然而，观察到在涉及大厅，超市和机场的大型开放环境时，准确性会降低很多。另一个问题是大型本地化区域引起的更高的硬件成本和计算负担。在本文中，我们分析了大开放环境中的每种技术的可行性，然后提出了一种实用的室内定位系统，其中PDR累积误差被稀疏部署的BLE灯泡间歇地减少。我们的贡献之一是提出一种基于单信标的定位方法，其基于RSS峰值和用户的行走方向。此外，我们采用强大的RSS峰值检测方法准确地识别真实的RSS峰值。同时，为了补偿RSS峰值检测的延迟，PDR用于推断相应的位移。最后，与建立经验公式的传统步长估计方法不同，我们提出了一种新的无参数方法，不需要离线训练，对每个人来说是可行的。实验结果表明，我们所提出的系统在3.5米范围内呈现90％的精度，在大开放环境中的总距离为377米。

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