Pub Date : 2023-11-17DOI: 10.1109/JISPIN.2023.3333852
Min Wang;Aurélie Dommes;Valérie Renaudin;Ni Zhu
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.
{"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}
We consider the robust localization, via Gaussian process regression (GPR), of multiple transmitters/targets based on received signal strength indicator (RSSI) data collected by fixed sensors distributed in the environment. For such a scenario and approach, we contribute both with a novel noise robust procedure to train the parameters of the GPR model, which is achieved via a mini-batch stochastic gradient descent (SGD) scheme with gradients given in closed form, and with a pair of corresponding robust marginalization procedures for the estimation of target locations. Simulation results validate the contributions by showing that the proposed methods significantly outperform the best related state-of-the-art (SotA) alternative and approach the performance of a genie-aided (GA) scheme.
{"title":"Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression","authors":"Niclas Führling;Hyeon Seok Rou;Giuseppe Thadeu Freitas de Abreu;David González G.;Osvaldo Gonsa","doi":"10.1109/JISPIN.2023.3332033","DOIUrl":"10.1109/JISPIN.2023.3332033","url":null,"abstract":"We consider the robust localization, via Gaussian process regression (GPR), of multiple transmitters/targets based on received signal strength indicator (RSSI) data collected by fixed sensors distributed in the environment. For such a scenario and approach, we contribute both with a novel noise robust procedure to train the parameters of the GPR model, which is achieved via a mini-batch stochastic gradient descent (SGD) scheme with gradients given in closed form, and with a pair of corresponding robust marginalization procedures for the estimation of target locations. Simulation results validate the contributions by showing that the proposed methods significantly outperform the best related state-of-the-art (SotA) alternative and approach the performance of a genie-aided (GA) scheme.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"104-114"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10314734","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610964","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}
Traditional pedestrian dead reckoning (PDR) systems have been designed for scenarios where users walk straight ahead. However, user behavior observation at the museum revealed that users often stop or walk sideways to look at the exhibits. If the user's smartphone is moving when the user is stopped, false step detection may occur. In addition, the correct step or change of direction may not be detected in sideways walking. To solve these problems, we propose a novel PDR system. First, we classify the user's walking style to address the problems of false step detection and undetected changes of direction. Next, we use a classifier to detect when the foot touches the ground from smartphone sensor data and perform step detection. Compared with the existing SmartPDR, our proposed method improved positioning accuracy by 20% in straight walking and 70% in sideways walking.
{"title":"Pedestrian Dead Reckoning for Multiple Walking Styles Using Classifier-Based Step Detection","authors":"Ibuki Yoshida;Takumi Suzaki;Hiroaki Murakami;Hiroki Watanabe;Mananari Nakamura;Hiromichi Hashizume;Masanori Sugimoto","doi":"10.1109/JISPIN.2023.3323937","DOIUrl":"10.1109/JISPIN.2023.3323937","url":null,"abstract":"Traditional pedestrian dead reckoning (PDR) systems have been designed for scenarios where users walk straight ahead. However, user behavior observation at the museum revealed that users often stop or walk sideways to look at the exhibits. If the user's smartphone is moving when the user is stopped, false step detection may occur. In addition, the correct step or change of direction may not be detected in sideways walking. To solve these problems, we propose a novel PDR system. First, we classify the user's walking style to address the problems of false step detection and undetected changes of direction. Next, we use a classifier to detect when the foot touches the ground from smartphone sensor data and perform step detection. Compared with the existing SmartPDR, our proposed method improved positioning accuracy by 20% in straight walking and 70% in sideways walking.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"69-79"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10285345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136305815","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}
Pub Date : 2023-10-12DOI: 10.1109/JISPIN.2023.3324279
Chi-Shih Jao;Danmeng Wang;Changwei Chen;Eudald Sangenis;Joe Grasso;Solmaz S. Kia;Andrei M. Shkel
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.
{"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}
Pub Date : 2023-07-27DOI: 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
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 $k$ -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.
{"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}
Pub Date : 2023-06-20DOI: 10.1109/JISPIN.2023.3287508
Wei Guo;Lei Jing
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 Wi -Fi M otion T racking ( WiMT ), 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 Z ero V elocity I dentification and C alibration ( ZVIC ) 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%.
{"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</b>\u0000-Fi \u0000<bold>M</b>\u0000otion \u0000<bold>T</b>\u0000racking (\u0000<bold>WiMT</b>\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</b>\u0000ero \u0000<bold>V</b>\u0000elocity \u0000<bold>I</b>\u0000dentification and \u0000<bold>C</b>\u0000alibration (\u0000<bold>ZVIC</b>\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}
Pub Date : 2023-04-26DOI: 10.1109/JISPIN.2023.3270123
Hanyuan Fu;Valérie Renaudin;Yacouba Kone;Ni Zhu
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.
{"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}
Pub Date : 2023-03-16DOI: 10.1109/JISPIN.2023.3276826
Ben Van Herbruggen;Stijn Luchie;Jaron Fontaine;Eli De Poorter
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.
{"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}
Pub Date : 2023-03-11DOI: 10.1109/JISPIN.2023.3275118
Ghazaleh Kia;David Plets;Ben Van Herbruggen;Eli De Poorter;Jukka Talvitie
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.
{"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}
Pub Date : 2023-01-01DOI: 10.1109/JISPIN.2024.3350445
{"title":"2023 Index IEEE Journal of Indoor and Seamless Positioning and Navigation Vol. 1","authors":"","doi":"10.1109/JISPIN.2024.3350445","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3350445","url":null,"abstract":"","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"254-261"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10381654","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139109421","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}
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