Toward Low-Overhead Fingerprint-Based Indoor Localization via Transfer Learning: Design, Implementation, and Evaluation
Toward Low-Overhead Fingerprint-Based Indoor Localization via Transfer Learning: Design, Implementation, and Evaluation
复制标题
通过迁移学习实现基于指纹的低开销室内定位：设计、实现和评估
DOI：
10.1109/tii.2017.2750240
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发表时间：
2018-03-01
影响因子：
12.3
通讯作者：
Son, Sang H.
中科院分区：
文献类型：
--
作者：
Liu, Kai;Zhang, Hao;Son, Sang H.
这项工作的目的是提出一个基于迁移学习（TL）的框架，以提高系统的可扩展性的指纹为基础的室内定位，通过减少离线训练开销，而不会危及定位精度。其基本原理是基于从源域转移的知识来重塑目标域中的数据分布，使得属于同一簇的那些数据在逻辑上彼此更接近，而其他数据将彼此更远。具体来说，基于TL的框架由度量学习和度量传递两部分组成，分别用于学习与源域的距离度量和识别最适合目标域的度量。此外，这项工作实现了一个原型的基于指纹的室内定位系统与建议的TL为基础的框架嵌入。最后，进行了广泛的真实世界的实验，以证明基于TL的框架的有效性和通用性。
This work aims at proposing a transfer learning (TL)-based framework to enhance system scalability of fingerprint-based indoor localization by reducing offline training overhead without jeopardizing the localization accuracy. The basic principle is to reshape data distributions in the target domain based on the transferred knowledge from the source domains, so that those data belonging to the same cluster will be logically closer to each other, whereas others will be further apart from each other. Specifically, the TL-based framework consists of two parts, metric learning and metric transfer, which are used to learn the distance metrics from source domains and identify the most suitable metric for the target domain, respectively. Furthermore, this work implements a prototype of the fingerprint-based indoor localization system with the proposed TL-based framework embedded. Finally, extensive real-world experiments are conducted to demonstrate the effectiveness and the generality of the TL-based framework.