[论文解读] A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Daoud Burghal, Ashwin T. Ravi+3 more|arXiv (Cornell University)|Dec 21, 2020

Indoor and Outdoor Localization Technologies参考文献 364被引用 42

一句话总结

本文综述基于机器学习的定位，利用射频信号，详细介绍系统架构、输入特征、ML方法、数据集和未解决的挑战。

ABSTRACT

The last few decades have witnessed a growing interest in location-based services. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both indoor and outdoor applications. However, challenges remain with respect to both complexity and accuracy of such systems. Machine Learning (ML) is one of the most promising methods for mitigating these problems, as ML (especially deep learning) offers powerful practical data-driven tools that can be integrated into localization systems. In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals. The survey spans different aspects, ranging from the system architectures, to the input features, the ML methods, and the datasets. A main point of the paper is the interaction between the domain knowledge arising from the physics of localization systems, and the various ML approaches. Besides the ML methods, the utilized input features play a major role in shaping the localization solution; we present a detailed discussion of the different features and what could influence them, be it the underlying wireless technology or standards or the preprocessing techniques. A detailed discussion is dedicated to the different ML methods that have been applied to localization problems, discussing the underlying problem and the solution structure. Furthermore, we summarize the different ways the datasets were acquired, and then list the publicly available ones. Overall, the survey categorizes and partly summarizes insights from almost 400 papers in this field. This survey is self-contained, as we provide a concise review of the main ML and wireless propagation concepts, which shall help the researchers in either field navigate through the surveyed solutions, and suggested open problems.

研究动机与目标

• 评估机器学习如何缓解室内外环境中射频定位的挑战。
• 总结影响基于ML的定位的输入特征、无线技术和标准。
• 对定位中的ML框架、数据可用性情景，以及由深度学习主导的方法进行分类。
• 突出公开数据集和实际挑战，为未来研究指明方向。

提出的方法

• 回顾机器学习基础和无线传播概念，以建立共同基线。
• 对定位方法（三边定位/到达时间 ToA、近邻、指纹、直接法）进行分类，以及ML如何与之整合。
• 调查输入特征及其与无线技术和标准的关系。
• 考察ML框架、数据可用性（监督、无监督、迁移学习）以及以DL为主的解决方案。
• 总结数据集并提出开放问题与未来工作方向。

实验结果

研究问题

• RQ1 在不同系统架构中应用于基于射频的定位的ML技术有哪些？
• RQ2 哪些输入特征和无线标准对基于ML的定位性能影响最大？
• RQ3 数据可用性和学习框架（有监督/无监督/迁移学习）如何影响ML定位解决方案？
• RQ4 存在哪些公开数据集，主要的开放挑战和研究方向是什么？

主要发现

• 基于ML的定位大致分为四类（三边定位/ ToA、近邻、指纹、直接法），ML增强或替代传统步骤。
• 特征选择和预处理对ML性能有显著影响，ML可以利用异构数据源。
• 深度学习推动了基于ML的定位的最近增长，许多最新方案利用DL架构。
• 公开数据集和数据获取方法被总结以支持可重复性和基准测试。
• 本文指出关键挑战（训练数据可用性、鲁棒性、设备上的实时计算、特征选择）并提出未来研究方向。