楼层识别的使能技术和工艺

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-07-17 DOI:10.1145/3678878
Imran Ashraf, Y. B. Zikria, Sahil Garg, Soojung Hur, Yongwan Park, Mohsen Guizani
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引用次数: 0

摘要

定位信息引发了众多应用,如基于位置的服务、医疗保健、应急响应和救援行动以及资产追踪。为确保提高水平和垂直方向的定位精度,人们提出了大量的技术和工艺。尽管有许多调查涉及水平定位技术,但文献中缺乏包含最新垂直定位方法的全面调查。本文详细介绍了不同的垂直定位技术,如路径损耗模型、到达时间、接收信号强度、参考信号接收功率,以及 WiFi、射频识别 (RFID)、全球移动通信系统 (GSM)、长期演进 (LTE)、气压计、惯性测量单元 (IMU) 传感器和地磁场所使用的指纹识别技术。本文的主要目的是利用智能手机在室内环境中进行人类定位。除了定位精度,本文还从成本、基础设施依赖性、部署复杂性和灵敏度等方面对所介绍的方法进行了评估。我们强调了这些方法的优缺点,并概述了未来的研究方向,以提高精确度,满足联邦通信委员会制定的楼层识别标准的未来需求。
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Enabling Technologies and Techniques for Floor Identification
Location information has initiated a multitude of applications such as location-based services, health care, emergency response and rescue operations, and assets tracking. A plethora of techniques and technologies have been presented to ensure enhanced location accuracy, both horizontal and vertical. Despite many surveys covering horizontal localization technologies, the literature lacks a comprehensive survey incorporating up-to-data vertical localization approaches. This paper provides a detailed survey of different vertical localization techniques such as path loss models, time of arrival, received signal strength, reference signal received power, and fingerprinting utilized by WiFi, radio frequency identification (RFID), global system for mobile communications (GSM), long term evolution (LTE), barometer, inertial measurement unit (IMU) sensors, and geomagnetic field. The paper primarily aims at human localization in indoor environments using smartphones in essence. Besides the localization accuracy, the presented approaches are evaluated in terms of cost, infrastructure dependence, deployment complexity, and sensitivity. We highlight the pros and cons of these approaches and outline future research directions to enhance the accuracy to meet the future needs of floor identification standards set by the Federal Communications Commission.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
期刊最新文献
How to Improve Video Analytics with Action Recognition: A Survey When Federated Learning Meets Privacy-Preserving Computation A review and benchmark of feature importance methods for neural networks Enabling Technologies and Techniques for Floor Identification A Comprehensive Analysis of Explainable AI for Malware Hunting
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