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A radiosity-based method to avoid calibration for indoor positioning systems

机译：基于光能传递的避免对室内定位系统进行校准的方法

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摘要

Due to the widespread use of mobile devices, services based on the users current indoor location are growing in significance. Such services are developed in the Machine Learning and Experst Systems realm, and ranges from guidance for blind people to mobile tourism and indoor shopping. One of the most used techniques for indoor positioning is WiFi fingerprinting, being its use of widespread WiFi signals one of the main reasons for its popularity, mostly on high populated urban areas. Most issues of this approach rely on the data acquisition phase; to manually sample WiFi RSSI signals in order to create a WiFi radio map is a high time consuming task, also subject to re-calibrations, because any change in the environment might affect the signal propagation, and therefore degrade the performance of the positioning system. The work presented in this paper aims at substituting the manual data acquisition phase by directly calculating the WiFi radio map by means of a radiosity signal propagation model. The time needed to acquire the WiFi radio map by means of the radiosity model dramatically reduces from hours to minutes when compared with manual acquisition. The proposed method is able to produce competitive results, in terms of accuracy, when compared with manual sampling, which can help domain experts develop services based on location faster. (C) 2018 Elsevier Ltd. All rights reserved.
机译：由于移动设备的广泛使用，基于用户当前室内位置的服务的重要性正在增长。这些服务是在机器学习和专家系统领域开发的，范围从盲人指导到移动旅游和室内购物。用于室内定位的最常用技术之一是WiFi指纹识别，这是它广泛使用WiFi信号的原因之一，其普及的主要原因之一是在人口稠密的城市地区。这种方法的大多数问题都依赖于数据采集阶段。手动采样WiFi RSSI信号以创建WiFi无线电图是一项非常耗时的任务，而且还需要重新校准，因为环境的任何变化都可能影响信号传播，从而降低定位系统的性能。本文提出的工作旨在通过使用光能传递信号模型直接计算WiFi无线电地图来替代手动数据获取阶段。与手动获取相比，借助光能传递模型获取WiFi无线电地图所需的时间从数小时减少到数分钟。与手动采样相比，该方法能够在准确性方面产生竞争性结果，这可以帮助领域专家更快地基于位置开发服务。 （C）2018 Elsevier Ltd.保留所有权利。

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