Design of aging-resistant Wi-Fi fingerprint-based localization system with continuous active learning

Youngsam Kim, Soohyung Kim
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引用次数: 3

Abstract

Wi-Fi fingerprint-based localization systems are widely used for indoor localization as it only needs Wi-Fi network infrastructure that exists almost everywhere nowadays. However, it can be vulnerable to environmental change if Wi-Fi fingerprint-based localization system uses fixed Wi-Fi fingerprint database as training dataset and has no method for updating training dataset. In this paper, we propose AR-WFL system including update phase that can reflect environmental change periodically and prevent performance degradation. The proposed AR-WFL system is based on crowdsourcing and no dedicated annotator exists. In addition, we adopt active learning scheme with uncertainty selective sampling algorithm to maximize cost-efficiency of the update phase. We evaluate the performance of the update phase as location estimation accuracy using a dataset we collected for 2 months. It shows that average accuracy is increased by 1.83%p using update phase with uncertainty sampling algorithm compared with the system without an update phase.
基于连续主动学习的抗老化Wi-Fi指纹定位系统设计
基于Wi-Fi指纹的定位系统由于只需要目前几乎无处不在的Wi-Fi网络基础设施,被广泛应用于室内定位。然而,如果基于Wi-Fi指纹的定位系统使用固定的Wi-Fi指纹数据库作为训练数据集,并且没有更新训练数据集的方法,则容易受到环境变化的影响。本文提出了包含更新阶段的AR-WFL系统,该系统可以周期性地反映环境变化并防止性能下降。提出的AR-WFL系统是基于众包的,不存在专门的注释器。此外,我们采用不确定选择采样算法的主动学习方案,以最大限度地提高更新阶段的成本效率。我们使用收集了2个月的数据集来评估更新阶段的性能和位置估计精度。结果表明,与不确定采样算法相比,采用更新相位的系统平均精度提高了1.83%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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