IEEE Journal of Indoor and Seamless Positioning and Navigation最新文献_第6页

Toward Low-Cost Passive Motion Tracking With One Pair of Commodity Wi-Fi Devices 用一对商品Wi-Fi设备实现低成本的被动运动跟踪

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-06-20 DOI: 10.1109/JISPIN.2023.3287508

Wei Guo;Lei Jing

{"title":"Toward Low-Cost Passive Motion Tracking With One Pair of Commodity Wi-Fi Devices","authors":"Wei Guo;Lei Jing","doi":"10.1109/JISPIN.2023.3287508","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3287508","url":null,"abstract":"With the popularity of Wi-Fi devices and the development of the Internet of Things (IoT), Wi-Fi-based passive motion tracking has attracted significant attention. Most existing works utilize the Angle of Arrival (AoA), Time of Flight (ToF), and Doppler Frequency Shift (DFS) of the Channel State Information (CSI) to track human motions. However, they usually require multiple pairs of Wi-Fi devices and extensive data training to achieve accurate results, which is unrealistic in practical applications. In this article, we propose \u0000<bold>Wi\u0000-Fi \u0000<bold>M\u0000otion \u0000<bold>T\u0000racking (\u0000<bold>WiMT\u0000), a low-cost passive motion tracking system based on a single pair of commodity Wi-Fi devices. WiMT calculates the Doppler velocity and phase difference using the CSI obtained from the transmitter with one antenna and the receiver with three antennas. The \u0000<bold>Z\u0000ero \u0000<bold>V\u0000elocity \u0000<bold>I\u0000dentification and \u0000<bold>C\u0000alibration (\u0000<bold>ZVIC\u0000) algorithm is proposed to remove the random noise of Doppler velocity when the target is stationary. We take the Doppler velocity as the measurement and employ a particle filter to estimate the motion trajectory. A particle weight update method based on phase difference information is developed to eliminate particles with low confidence. Experimental results in real indoor environment show that WiMT achieves great performance with a motion tracking median error of 7.28 cm and a nonmoving recognition accuracy of 92.6%.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"39-52"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9955032/9962767/10158358.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50323046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors 基于可穿戴惯性传感器的行人导航人工智能分析

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-04-26 DOI: 10.1109/JISPIN.2023.3270123

Hanyuan Fu;Valérie Renaudin;Yacouba Kone;Ni Zhu

{"title":"Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors","authors":"Hanyuan Fu;Valérie Renaudin;Yacouba Kone;Ni Zhu","doi":"10.1109/JISPIN.2023.3270123","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3270123","url":null,"abstract":"Wearable devices embedding inertial sensors enable autonomous, seamless, and low-cost pedestrian navigation. As appealing as it is, the approach faces several challenges: measurement noises, different device-carrying modes, different user dynamics, and individual walking characteristics. Recent research applies artificial intelligence (AI) to improve inertial navigation's robustness and accuracy. Our analysis identifies two main categories of AI approaches depending on the inertial signals segmentation: 1) either using human gait events (steps or strides) or 2) fixed-length inertial data segments. A theoretical analysis of the fundamental assumptions is carried out for each category. Two state-of-the-art AI algorithms (SELDA, RoNIN), representative of each category, and a gait-driven non-AI method (SmartWalk) are evaluated in a 2.17-km-long open-access dataset, representative of the diversity of pedestrians' mobility surroundings (open-sky, indoors, forest, urban, parking lot). SELDA is an AI-based stride length estimation algorithm, RoNIN is an AI-based positioning method, and SmartWalk is a gait-driven non-AI positioning method. The experimental assessment shows the distinct features in each category and their limits with respect to the underlying hypotheses. On average, SELDA, RoNIN, and SmartWalk achieve 8-m, 22-m, and 17-m average positioning errors (RMSE), respectively, on six testing tracks recorded with two volunteers in various environments.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"26-38"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9955032/9962767/10108968.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50323045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multihop Self-Calibration Algorithm for Ultra-Wideband (UWB) Anchor Node Positioning 超宽带锚节点定位的多跳自校准算法

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-03-16 DOI: 10.1109/JISPIN.2023.3276826

Ben Van Herbruggen;Stijn Luchie;Jaron Fontaine;Eli De Poorter

{"title":"Multihop Self-Calibration Algorithm for Ultra-Wideband (UWB) Anchor Node Positioning","authors":"Ben Van Herbruggen;Stijn Luchie;Jaron Fontaine;Eli De Poorter","doi":"10.1109/JISPIN.2023.3276826","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3276826","url":null,"abstract":"Ultra-wideband (UWB) is an emerging technology for indoor localization systems with high accuracy and excellent resilience against multipath fading and interference from other technologies. However, UWB localization systems require the installation of infrastructure devices (anchor nodes) with known positions to serve as reference points. These coordinates are of utmost importance for the performance of the indoor localization system as the position of the mobile tag(s) will be calculated based on this information. Currently most large-scale systems require manual measurement of the anchor coordinates, which is a time-consuming and error-prone process. Therefore, we propose an algorithmic approach whereby based on measurements of the position of a small random chosen subset of anchors, the position of all other anchors is calculated automatically by collecting distances between all anchors with two-way-ranging UWB. In this article we present a three stage algorithm which contains: 1) an initialization phase; 2) a global optimization phase; and 3) an optional extra calibration phase with a mobile node. In contrast to related work, our approach also works in multihop environments with severe non-line-of-sight effects. In a real world multihop Industry 4.0 environment with metal racks as obstacles and 18 UWB nodes, the algorithm is able to localize the anchors with an mean absolute error of only 21.6 cm.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9955032/9962767/10124958.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50323188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward Seamless Localization: Situational Awareness Using UWB Wearable Systems and Convolutional Neural Networks 走向无缝定位：使用UWB可穿戴系统和卷积神经网络的态势感知

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-03-11 DOI: 10.1109/JISPIN.2023.3275118

Ghazaleh Kia;David Plets;Ben Van Herbruggen;Eli De Poorter;Jukka Talvitie

{"title":"Toward Seamless Localization: Situational Awareness Using UWB Wearable Systems and Convolutional Neural Networks","authors":"Ghazaleh Kia;David Plets;Ben Van Herbruggen;Eli De Poorter;Jukka Talvitie","doi":"10.1109/JISPIN.2023.3275118","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3275118","url":null,"abstract":"Depending on the environment, an increasing number of localization methods are available ranging from satellite-based localization to visual navigation, each with its own advantages and disadvantages. Fast and reliable identification of the environment characteristics is crucial for selecting the best available localization method. This research introduces a deep-learning-based method utilizing data collected with wearable ultra-wideband devices. A novel approach mimicking radar behavior is presented to collect the relevant data. Channel state information is proposed for training of the neural network and enabling the environment detection to obtain the desired situational awareness. The proposed detection approach is evaluated in three types of environments: 1) indoor, 2) open outdoor, and 3) crowded urban. The results show that fast and accurate environment detection for seamless localization purposes can be achieved with a precision of 91% for general scenarios and a precision of 96% for specific use cases.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"12-25"},"PeriodicalIF":0.0,"publicationDate":"2023-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9955032/9962767/10122970.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50415636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2023 Index IEEE Journal of Indoor and Seamless Positioning and Navigation Vol. 1 2023 Index IEEE Journal of Indoor and Seamless Positioning and Navigation Vol.

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-01-01 DOI: 10.1109/JISPIN.2024.3350445

引用次数: 0

IEEE Journal of Indoor and Seamless Positioning and Navigation Publication Information IEEE室内无缝定位导航杂志出版信息

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2022-11-23 DOI: 10.1109/JISPIN.2022.3211748

引用次数: 0

“Set of Rooms” Technologies “房间集”技术

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2019-07-10 DOI: 10.1002/9781119421887.ch7

引用次数: 0

Various Possible Classifications of Indoor Technologies 各种可能的室内技术分类

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2019-07-10 DOI: 10.1002/9781119421887.ch4

引用次数: 0

Combining Techniques and Technologies 技术与技术相结合

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2019-07-10 DOI: 10.1002/9781119421887.ch12

引用次数: 0

General Introduction to Positioning Techniques and Their Associated Difficulties 定位技术及其相关难点概述

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2019-07-10 DOI: 10.1002/9781119421887.ch3

引用次数: 0