IEEE Journal of Indoor and Seamless Positioning and Navigation最新文献_第5页

Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme for GNSS-Deprived Zones 双向 UWB 定位：全球导航卫星系统匮乏地区弹性定位方案综述

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-11-27 DOI: 10.1109/JISPIN.2023.3337055

Cung Lian Sang;Michael Adams;Marc Hesse;Ulrich Rückert

{"title":"Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme for GNSS-Deprived Zones","authors":"Cung Lian Sang;Michael Adams;Marc Hesse;Ulrich Rückert","doi":"10.1109/JISPIN.2023.3337055","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3337055","url":null,"abstract":"A bidirectional ultrawideband (UWB) localization scheme is one of the three widely adopted design integration processes commonly used in time-based UWB positioning systems. The key property of bidirectional UWB localization is its ability to serve both navigation and tracking tasks within a single localization scheme on demand. Traditionally, navigation and tracking in wireless localization systems were treated as separate entities due to distinct applicable use-cases and methodological needs in each implementation process. Therefore, the ability to flexibly or elastically combine two unique positioning perspectives (navigation and tracking) within a single scheme can be regarded as a paradigm shift in the way location-based services are conventionally observed. This article reviews the mentioned bidirectional UWB localization from the perspective of a flexible and versatile positioning topology and highlights its potential in the field. In this regard, the article comprehensively describes the complete system model of the bidirectional UWB localization scheme using modular processes. It also discusses the demonstrative evaluation of two system integration processes and conducts a strengths, weaknesses, opportunities, and threats analysis of the scheme. Furthermore, the prospect of the presented bidirectional localization scheme for achieving precise location estimation in 5G/6G wireless mobile networks, as well as in Wi-Fi fine-time measurement-based positioning systems was briefly discussed.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"161-179"},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10328864","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138678660","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

UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests 重新审视 UJI 探测：深入挖掘 Wi-Fi 探测请求数据集

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-11-22 DOI: 10.1109/JISPIN.2023.3335882

Tomas Bravenec;Joaquín Torres-Sospedra;Michael Gould;Tomas Fryza

{"title":"UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests","authors":"Tomas Bravenec;Joaquín Torres-Sospedra;Michael Gould;Tomas Fryza","doi":"10.1109/JISPIN.2023.3335882","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3335882","url":null,"abstract":"This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"221-230"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10325607","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822153","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

Drone Navigation and Target Interception Using Deep Reinforcement Learning: A Cascade Reward Approach 利用深度强化学习进行无人机导航和目标拦截：级联奖励方法

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-11-20 DOI: 10.1109/JISPIN.2023.3334690

Ali A. Darwish;Arie Nakhmani

{"title":"Drone Navigation and Target Interception Using Deep Reinforcement Learning: A Cascade Reward Approach","authors":"Ali A. Darwish;Arie Nakhmani","doi":"10.1109/JISPIN.2023.3334690","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3334690","url":null,"abstract":"This article proposes an architecture for drone navigation and target interception, utilizing a self-supervised, model-free deep reinforcement learning approach. Unlike the traditional methods relying on complex controllers, our approach uses deep reinforcement learning with cascade rewards, enabling a single drone to navigate obstacles and intercept targets using only a forward-facing depth–RGB camera. This research has significant implications for robotics, as it demonstrates how complex tasks can be tackled using deep reinforcement learning. Our work encompasses three key contributions. First, we tackle the challenge of partial observability when employing nonlinear function approximators for learning stochastic policies. Second, we optimize the task of maximizing the overall expected reward. Finally, we develop a software library for training drones to track and intercept targets. Through our experiments, we demonstrated that our approach, incorporating cascade reward, outperforms state-of-the-art deep \u0000<italic>Q</i>\u0000-network algorithms in terms of learning policies. By leveraging our methodology, drones can successfully navigate complex indoor and outdoor environments and effectively intercept targets based on visual cues.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"130-140"},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633905","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

MoRPI: Mobile Robot Pure Inertial Navigation MoRPI：移动机器人纯惯性导航

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-11-20 DOI: 10.1109/JISPIN.2023.3334697

Aviad Etzion;Itzik Klein

引用次数: 0

STELLAR: Siamese Multiheaded Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity With Indoor Localization STELLAR：利用室内定位克服时变和设备异质性的连体多头注意力神经网络

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-11-20 DOI: 10.1109/JISPIN.2023.3334693

Danish Gufran;Saideep Tiku;Sudeep Pasricha

{"title":"STELLAR: Siamese Multiheaded Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity With Indoor Localization","authors":"Danish Gufran;Saideep Tiku;Sudeep Pasricha","doi":"10.1109/JISPIN.2023.3334693","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3334693","url":null,"abstract":"Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy. Toward jointly addressing these challenges comprehensively, we propose STELLAR, a novel framework implementing a contrastive learning approach that leverages a Siamese multiheaded attention neural network. STELLAR is the first solution that simultaneously tackles device heterogeneity and temporal variations in indoor localization, without the need for retraining the model (recalibration-free). Our evaluations across diverse indoor environments show 8%–75% improvements in accuracy compared to state-of-the-art techniques, to effectively address the device heterogeneity challenge. Moreover, STELLAR outperforms existing methods by 18%–165% over two years of temporal variations, showcasing its robustness and adaptability.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"115-129"},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138491035","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

Analysis of Spatial Landmarks for Seamless Urban Navigation of Visually Impaired People 视障人士城市无缝导航的空间地标分析

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-11-17 DOI: 10.1109/JISPIN.2023.3333852

Min Wang;Aurélie Dommes;Valérie Renaudin;Ni Zhu

{"title":"Analysis of Spatial Landmarks for Seamless Urban Navigation of Visually Impaired People","authors":"Min Wang;Aurélie Dommes;Valérie Renaudin;Ni Zhu","doi":"10.1109/JISPIN.2023.3333852","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3333852","url":null,"abstract":"Navigating in urban environment is a major challenge for visually impaired people. Spatial landmarks are crucial for them to orient and navigate in their environment. In this paper, the spatial landmarks most important and commonly used by visually impaired people are identified through interviews, and geometric constraints of these landmarks are constructed to facilitate the development of map-matching algorithms. Interviews were conducted with 12 visually impaired people who had a range of visual impairments and used various mobility aids. Data were analyzed by sensory modality, occurrence of use, and number of users. 14 main landmarks for urban navigation were selected and categorized into two groups: Waypoints and Reassurance Points, depending on whether they are directly detected by touch. Geometric constraints were developed for each landmark to prepare their integration into map-matching or path-planning algorithms. The result is a comprehensive dictionary of landmarks and their geometric constraints is created, specifically tailored to help visually impaired people navigate urban environments. Our user-centric approach successfully translates the subjective navigation experiences of visually impaired people into an objective, universally accessible format. This bridges the gap between personal experiences and practical applications and paves the way for more inclusive navigation solutions for visually impaired people in urban environments.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"93-103"},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10320446","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138485032","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

Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression 基于鲁棒接收信号强度指标的高斯过程回归多目标定位

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-11-10 DOI: 10.1109/JISPIN.2023.3332033

Niclas Führling;Hyeon Seok Rou;Giuseppe Thadeu Freitas de Abreu;David González G.;Osvaldo Gonsa

引用次数: 0

Pedestrian Dead Reckoning for Multiple Walking Styles Using Classifier-Based Step Detection 基于分类器步长检测的多种步行方式行人航位推算

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-10-13 DOI: 10.1109/JISPIN.2023.3323937

Ibuki Yoshida;Takumi Suzaki;Hiroaki Murakami;Hiroki Watanabe;Mananari Nakamura;Hiromichi Hashizume;Masanori Sugimoto

引用次数: 0

Augmented UWB-ZUPT-SLAM Utilizing Multisensor Fusion 利用多传感器融合的增强UWB-ZUPT-SLAM

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-10-12 DOI: 10.1109/JISPIN.2023.3324279

Chi-Shih Jao;Danmeng Wang;Changwei Chen;Eudald Sangenis;Joe Grasso;Solmaz S. Kia;Andrei M. Shkel

{"title":"Augmented UWB-ZUPT-SLAM Utilizing Multisensor Fusion","authors":"Chi-Shih Jao;Danmeng Wang;Changwei Chen;Eudald Sangenis;Joe Grasso;Solmaz S. Kia;Andrei M. Shkel","doi":"10.1109/JISPIN.2023.3324279","DOIUrl":"10.1109/JISPIN.2023.3324279","url":null,"abstract":"This article proposes a generalized UltraWideBand (UWB)-Zero-velocity-UPdaTe (ZUPT)-simultaneous localization and mapping (SLAM) algorithm, a SLAM approach, utilizing a combination of foot-mounted localization systems integrating inertial measurement units (IMUs), UWB modules, barometers, and dynamically-deployed beacons incorporating UWB, IMUs, and reference barometers. The proposed approach leverages a ZUPT-aided Inertial Navigation System augmented with self-contained sensor fusion techniques to map unknown UWB beacons dynamically deployed in an environment during navigation and then utilizes the localized beacons to bound position error propagation. An experimental testbed was developed, and we conducted two series of experiments to validate the performance of the proposed approach. The first experiment involved high-accuracy motion capture cameras in generating ground truth, and the results showed that the proposed approach estimated positions of UWB beacons with a maximum localization error of 0.36 m, when deployed during the first 15 and 20 s of the navigation. In the second experiment, a pedestrian traveled for around 3.5 km in 1 h in a large multifloor indoor environment and deployed seven beacons, during the first 63, 151, 290, 399, 517, 585, and 786 s of the experiment. The proposed generalized UWB-ZUPT-SLAM had a 3-D mean absolute error of 0.48 m in this experiment, equivalent to 0.013% traveling distance.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"80-92"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10283865","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302860","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

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive 用于基于指纹定位的自动编码器极限学习机：良好的权重初始化是决定性的

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2023-07-27 DOI: 10.1109/JISPIN.2023.3299433

Darwin P. Quezada Gaibor;Lucie Klus;Roman Klus;Elena Simona Lohan;Jari Nurmi;Mikko Valkama;Joaquín Huerta;Joaquín Torres-Sospedra

{"title":"Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive","authors":"Darwin P. Quezada Gaibor;Lucie Klus;Roman Klus;Elena Simona Lohan;Jari Nurmi;Mikko Valkama;Joaquín Huerta;Joaquín Torres-Sospedra","doi":"10.1109/JISPIN.2023.3299433","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3299433","url":null,"abstract":"Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"53-68"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9955032/9962767/10195972.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50323047","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