Pub Date : 2024-01-26DOI: 10.1109/JISPIN.2024.3359151
Andrey Sesyuk;Stelios Ioannou;Marios Raspopoulos
The 3-D nature of modern smart applications has imposed significant 3-D positioning accuracy requirements, especially in indoor environments. However, a major limitation of most existing indoor localization systems is their focus on estimating positions mainly in the horizontal plane, overlooking the crucial vertical dimension. This neglect presents considerable challenges in accurately determining the 3-D position of devices, such as drones and individuals across multiple floors of a building let alone the cm-level accuracy that might be required in many of these applications. To tackle this issue, millimeter-wave (mmWave) positioning systems have emerged as a promising technology offering high accuracy and robustness even in complex indoor environments. This article aims to leverage the potential of mmWave radar technology to achieve precise ranging and angling measurements presenting a comprehensive methodology for evaluating the performance of mmWave sensors in terms of measurement precision while demonstrating the 3-D positioning accuracy that can be achieved. The main challenges and the respective solutions associated with the use of mmWave sensors for indoor positioning are highlighted, providing valuable insights into their potentials and suitability for practical applications.
{"title":"Radar-Based Millimeter-Wave Sensing for Accurate 3-D Indoor Positioning: Potentials and Challenges","authors":"Andrey Sesyuk;Stelios Ioannou;Marios Raspopoulos","doi":"10.1109/JISPIN.2024.3359151","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3359151","url":null,"abstract":"The 3-D nature of modern smart applications has imposed significant 3-D positioning accuracy requirements, especially in indoor environments. However, a major limitation of most existing indoor localization systems is their focus on estimating positions mainly in the horizontal plane, overlooking the crucial vertical dimension. This neglect presents considerable challenges in accurately determining the 3-D position of devices, such as drones and individuals across multiple floors of a building let alone the cm-level accuracy that might be required in many of these applications. To tackle this issue, millimeter-wave (mmWave) positioning systems have emerged as a promising technology offering high accuracy and robustness even in complex indoor environments. This article aims to leverage the potential of mmWave radar technology to achieve precise ranging and angling measurements presenting a comprehensive methodology for evaluating the performance of mmWave sensors in terms of measurement precision while demonstrating the 3-D positioning accuracy that can be achieved. The main challenges and the respective solutions associated with the use of mmWave sensors for indoor positioning are highlighted, providing valuable insights into their potentials and suitability for practical applications.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"61-75"},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10415170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987099","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}
Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations; ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site—which were impossible to organize during 2021 and 2022—in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.
{"title":"Offsite Evaluation of Localization Systems: Criteria, Systems, and Results From IPIN 2021 and 2022 Competitions","authors":"Francesco Potortì;Antonino Crivello;Soyeon Lee;Blagovest Vladimirov;Sangjoon Park;Yushi Chen;Long Wang;Runze Chen;Fang Zhao;Yue Zhuge;Haiyong Luo;Antoni Perez-Navarro;Antonio R. Jiménez;Han Wang;Hengyi Liang;Cedric De Cock;David Plets;Yan Cui;Zhi Xiong;Xiaodong Li;Yiming Ding;Fernando Javier Álvarez Franco;Fernando Jesús Aranda Polo;Felipe Parralejo Rodríguez;Adriano Moreira;Cristiano Pendão;Ivo Silva;Miguel Ortiz;Ni Zhu;Ziyou Li;Valérie Renaudin;Dongyan Wei;Xinchun Ji;Wenchao Zhang;Yan Wang;Longyang Ding;Jian Kuang;Xiaobing Zhang;Zhi Dou;Chaoqun Yang;Sebastian Kram;Maximilian Stahlke;Christopher Mutschler;Sander Coene;Chenglong Li;Alexander Venus;Erik Leitinger;Stefan Tertinek;Klaus Witrisal;Yi Wang;Shaobo Wang;Beihong Jin;Fusang Zhang;Chang Su;Zhi Wang;Siheng Li;Xiaodong Li;Shitao Li;Mengguan Pan;Wang Zheng;Kai Luo;Ziyao Ma;Yanbiao Gao;Jiaxing Chang;Hailong Ren;Wenfang Guo;Joaquín Torres-Sospedra","doi":"10.1109/JISPIN.2024.3355840","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3355840","url":null,"abstract":"Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations; ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site—which were impossible to organize during 2021 and 2022—in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"92-129"},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10404047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297043","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 : 2024-01-16DOI: 10.1109/JISPIN.2024.3354248
Chaitra Hegde;Yashar Kiarashi;Amy D. Rodriguez;Allan I. Levey;Matthew Doiron;Hyeokhyen Kwon;Gari D. Clifford
Social interaction behaviors change as a result of both physical and psychiatric problems, and it is important to identify subtle changes in group activity engagements for monitoring the mental health of patients in clinics. This work proposes a system to identify when and where group formations occur in an approximately 1700 $ text{m}^{2}$ therapeutic built environment using a distributed edge-computing camera network. The proposed method can localize group formations when provided with noisy positions and orientations of individuals, estimated from sparsely distributed multiview cameras, which run a lightweight multiperson 2-D pose detection model. Our group identification method demonstrated an F1 score of up to 90% with a mean absolute error of 1.25 m for group localization on our benchmark dataset. The dataset consisted of seven subjects walking, sitting, and conversing for 35 min in groups of various sizes ranging from 2 to 7 subjects. The proposed system is low-cost and scalable to any ordinary building to transform the indoor space into a smart environment using edge computing systems. We expect the proposed system to enhance existing therapeutic units for passively monitoring the social behaviors of patients when implementing real-time interventions.
{"title":"Indoor Group Identification and Localization Using Privacy-Preserving Edge Computing Distributed Camera Network","authors":"Chaitra Hegde;Yashar Kiarashi;Amy D. Rodriguez;Allan I. Levey;Matthew Doiron;Hyeokhyen Kwon;Gari D. Clifford","doi":"10.1109/JISPIN.2024.3354248","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3354248","url":null,"abstract":"Social interaction behaviors change as a result of both physical and psychiatric problems, and it is important to identify subtle changes in group activity engagements for monitoring the mental health of patients in clinics. This work proposes a system to identify when and where group formations occur in an approximately 1700 \u0000<inline-formula><tex-math>$ text{m}^{2}$</tex-math></inline-formula>\u0000 therapeutic built environment using a distributed edge-computing camera network. The proposed method can localize group formations when provided with noisy positions and orientations of individuals, estimated from sparsely distributed multiview cameras, which run a lightweight multiperson 2-D pose detection model. Our group identification method demonstrated an F1 score of up to 90% with a mean absolute error of 1.25 m for group localization on our benchmark dataset. The dataset consisted of seven subjects walking, sitting, and conversing for 35 min in groups of various sizes ranging from 2 to 7 subjects. The proposed system is low-cost and scalable to any ordinary building to transform the indoor space into a smart environment using edge computing systems. We expect the proposed system to enhance existing therapeutic units for passively monitoring the social behaviors of patients when implementing real-time interventions.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"51-60"},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738932","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 : 2023-12-27DOI: 10.1109/JISPIN.2023.3344291
Valérie Renaudin;Francesco Potorti
It is with great pleasure that we introduce the first issue of the IEEE Journal on Indoor and Seamless Positioning and Navigation (J-ISPIN). J-ISPIN is a gold open-access publication of the IEEE Sensors Council, the IEEE Signal Processing Society, and the Instrumentation and Measurement Society. The multidisciplinary J-ISPIN provides a platform for Open Access publishing in response to the growing demand for Open Access. Thus, this first issue represents an important milestone for indoor and seamless positioning publishing.
{"title":"Editorial An Important Step for Indoor and Seamless Positioning and Navigation","authors":"Valérie Renaudin;Francesco Potorti","doi":"10.1109/JISPIN.2023.3344291","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3344291","url":null,"abstract":"It is with great pleasure that we introduce the first issue of the \u0000<sc>IEEE Journal on Indoor and Seamless Positioning and Navigation</small>\u0000 (J-ISPIN). J-ISPIN is a gold open-access publication of the IEEE Sensors Council, the IEEE Signal Processing Society, and the Instrumentation and Measurement Society. The multidisciplinary J-ISPIN provides a platform for Open Access publishing in response to the growing demand for Open Access. Thus, this first issue represents an important milestone for indoor and seamless positioning publishing.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"iii-iii"},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050652","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}
Radio-frequency technologies have been largely explored to deliver reliable indoor localization systems. However, at the current stage, none of the proposed technologies represent a de-facto standard. Although RSS-based (received signal strength) techniques have been extensively studied, they suffer of a number of side-effects mainly caused by the complexity of radio propagation in indoor environments. A possible solution is designing systems exploiting multiple techniques, so that to compensate weaknesses of a specific source of information. Under this respect, Bluetooth represents an interesting technology, combining multiple techniques for indoor localization. In particular, the BT5.1 direction finding specification includes the possibility of estimating the angle between an emitting device and an antenna array. The Angle of Arrival (AoA) provides interesting features for the localization purpose, as it allows estimating the direction from which a signal is propagated. In this work, we detail our experimental setting based on a BT5.1-compliant kit to quantitatively measure the performance in three scenarios: static positioning, mobility, and proximity detection. Scenarios provide a robust benchmark allowing us to identify and discuss features of AoA values also in comparison with respect to traditional RSS-based approaches.
{"title":"An Experimental Evaluation Based on Direction Finding Specification for Indoor Localization and Proximity Detection","authors":"Michele Girolami;Fabio Mavilia;Francesco Furfari;Paolo Barsocchi","doi":"10.1109/JISPIN.2023.3345268","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3345268","url":null,"abstract":"Radio-frequency technologies have been largely explored to deliver reliable indoor localization systems. However, at the current stage, none of the proposed technologies represent a de-facto standard. Although RSS-based (received signal strength) techniques have been extensively studied, they suffer of a number of side-effects mainly caused by the complexity of radio propagation in indoor environments. A possible solution is designing systems exploiting multiple techniques, so that to compensate weaknesses of a specific source of information. Under this respect, Bluetooth represents an interesting technology, combining multiple techniques for indoor localization. In particular, the BT5.1 direction finding specification includes the possibility of estimating the angle between an emitting device and an antenna array. The Angle of Arrival (AoA) provides interesting features for the localization purpose, as it allows estimating the direction from which a signal is propagated. In this work, we detail our experimental setting based on a BT5.1-compliant kit to quantitatively measure the performance in three scenarios: static positioning, mobility, and proximity detection. Scenarios provide a robust benchmark allowing us to identify and discuss features of AoA values also in comparison with respect to traditional RSS-based approaches.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"36-50"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10366502","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139109574","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 : 2023-12-18DOI: 10.1109/JISPIN.2023.3344077
Ilari Pajula;Niclas Joswig;Aiden Morrison;Nadia Sokolova;Laura Ruotsalainen
Visual–inertial odometry (VIO), the fusion of visual and inertial sensor data, has been shown to be functional for navigation in global-navigation-satellite-system-denied environments. Recently, dense-optical-flow-based end-to-end trained deep learning VIO models have gained superior performance in outdoor navigation. In this article, we introduced a novel visual–inertial sensor fusion approach based on vision transformer architecture with a cross-attention mechanism, specifically designed to better integrate potentially poor-quality optical flow features with inertial data. Although optical-flow-based VIO models have obtained superior performance in outdoor vehicle navigation, both in accuracy and ease of calibration, we have shown how their suitability for indoor pedestrian navigation is still far from existing feature-matching-based methods. We compare the performance of traditional VIO models against deep-learning-based VIO models on the KITTI benchmark dataset and our custom pedestrian navigation dataset. We show how end-to-end trained VIO models using optical flow were significantly outperformed by simpler visual odometry models utilizing feature matching. Our findings indicate that due to the robustness against occlusion and camera shake, feature matching is better suited for indoor pedestrian navigation, whereas dense optical flow remains viable for vehicular data. Therefore, the most feasible way forward will be the integration of our novel model with feature-based visual data encoding.
{"title":"A Novel Cross-Attention-Based Pedestrian Visual–Inertial Odometry With Analyses Demonstrating Challenges in Dense Optical Flow","authors":"Ilari Pajula;Niclas Joswig;Aiden Morrison;Nadia Sokolova;Laura Ruotsalainen","doi":"10.1109/JISPIN.2023.3344077","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3344077","url":null,"abstract":"Visual–inertial odometry (VIO), the fusion of visual and inertial sensor data, has been shown to be functional for navigation in global-navigation-satellite-system-denied environments. Recently, dense-optical-flow-based end-to-end trained deep learning VIO models have gained superior performance in outdoor navigation. In this article, we introduced a novel visual–inertial sensor fusion approach based on vision transformer architecture with a cross-attention mechanism, specifically designed to better integrate potentially poor-quality optical flow features with inertial data. Although optical-flow-based VIO models have obtained superior performance in outdoor vehicle navigation, both in accuracy and ease of calibration, we have shown how their suitability for indoor pedestrian navigation is still far from existing feature-matching-based methods. We compare the performance of traditional VIO models against deep-learning-based VIO models on the KITTI benchmark dataset and our custom pedestrian navigation dataset. We show how end-to-end trained VIO models using optical flow were significantly outperformed by simpler visual odometry models utilizing feature matching. Our findings indicate that due to the robustness against occlusion and camera shake, feature matching is better suited for indoor pedestrian navigation, whereas dense optical flow remains viable for vehicular data. Therefore, the most feasible way forward will be the integration of our novel model with feature-based visual data encoding.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"25-35"},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10363184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406582","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 : 2023-12-14DOI: 10.1109/JISPIN.2023.3343336
Haige Chen;Ashutosh Dhekne
This article presents UnSpoof, an ultrawideband localization system that can detect and localize distance-spoofing tags with a few collaborative passively receiving anchors. We propose novel formulations that enable passively receiving anchors to deduce their time-of-flight (ToF) and time-difference-of-arrival (TDoA) just by overhearing standard two-way ranging messages between the tag and one active anchor. Our ToF formulation can be used to precisely localize an honest tag, and to detect a distance-spoofing tag that falsely reports its timestamps. Additionally, our TDoA formulation enables spoofing deterrent localization, which can be used to track down and apprehend a malicious tag. Our experimental evaluation shows a 30-cm $text {75}{text{th}}$ percentile error for ToF-based honest tag localization and a submeter error for TDoA-based localization for spoofing tags. We demonstrate successful detection of distance reduction and enlargement attacks inside the anchors' convex hull and graceful degradation outside. In addition, we show the effects of a nonregular geometry of anchors and invite researchers and practitioners to experiment with anchor topologies of interest to them via our open source modeling software.
{"title":"Spoofing Evident and Spoofing Deterrent Localization Using Ultrawideband (UWB) Active–Passive Ranging","authors":"Haige Chen;Ashutosh Dhekne","doi":"10.1109/JISPIN.2023.3343336","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3343336","url":null,"abstract":"This article presents UnSpoof, an ultrawideband localization system that can detect and localize distance-spoofing tags with a few collaborative passively receiving anchors. We propose novel formulations that enable passively receiving anchors to deduce their time-of-flight (ToF) and time-difference-of-arrival (TDoA) just by overhearing standard two-way ranging messages between the tag and one active anchor. Our ToF formulation can be used to precisely localize an honest tag, and to detect a distance-spoofing tag that falsely reports its timestamps. Additionally, our TDoA formulation enables spoofing deterrent localization, which can be used to track down and apprehend a malicious tag. Our experimental evaluation shows a 30-cm \u0000<inline-formula><tex-math>$text {75}{text{th}}$</tex-math></inline-formula>\u0000 percentile error for ToF-based honest tag localization and a submeter error for TDoA-based localization for spoofing tags. We demonstrate successful detection of distance reduction and enlargement attacks inside the anchors' convex hull and graceful degradation outside. In addition, we show the effects of a nonregular geometry of anchors and invite researchers and practitioners to experiment with anchor topologies of interest to them via our open source modeling software.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"12-24"},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10360231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081260","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}
The power over Ethernet (PoE)-enabled visible light positioning (VLP) networks as a promising technology can significantly enhance accuracy and cost-effectiveness of indoor positioning. However, both the limited bandwidth of the light-emitting diode (LED) and the low sampling rate of the receiver have a negative impact on the positioning performance. Moreover, time synchronization requirements between transmitters and between transceivers become more stringent in a resource-constrained VLP network. To address these issues, a PoE-enabled VLP scheme with low bandwidth requirement and high-precision pulse reconstruction is proposed in this article. Specifically, the precision time protocol and synchronous Ethernet are introduced to realize the synchronization transmission. Meanwhile, an on–off keying (OOK) modulation-based beacon signal is designed to unlock both the transceivers' synchronization and bandwidth requirements. Then, a high-precision pulse reconstruction method considering the LED model and impulse response is established to enhance the signal quality. Moreover, the position is estimated based on the maximum a posteriori (MAP) probability criterion. Experimental results obtained by the VLP testbed demonstrate that the proposed scheme outperforms the benchmark positioning schemes. It achieves a positioning accuracy of 1.7 cm by using the reconstructed 2 GHz sampling rate in the case of a bandwidth of 50 MHz and a real sampling rate of 100 MHz. Last but not least, the proposed scheme maintains positioning accuracy within 30 cm even with a few MHz bandwidth of LED.
{"title":"PoE-Enabled Visible Light Positioning Network With Low Bandwidth Requirement and High Precision Pulse Reconstruction","authors":"Zhenghai Wang;Xuan Huang;Xuanbang Chen;Mengzhen Xu;Xiaodong Liu;Yuhao Wang;Xun Zhang","doi":"10.1109/JISPIN.2023.3342732","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3342732","url":null,"abstract":"The power over Ethernet (PoE)-enabled visible light positioning (VLP) networks as a promising technology can significantly enhance accuracy and cost-effectiveness of indoor positioning. However, both the limited bandwidth of the light-emitting diode (LED) and the low sampling rate of the receiver have a negative impact on the positioning performance. Moreover, time synchronization requirements between transmitters and between transceivers become more stringent in a resource-constrained VLP network. To address these issues, a PoE-enabled VLP scheme with low bandwidth requirement and high-precision pulse reconstruction is proposed in this article. Specifically, the precision time protocol and synchronous Ethernet are introduced to realize the synchronization transmission. Meanwhile, an \u0000<sc>on–off</small>\u0000 keying (OOK) modulation-based beacon signal is designed to unlock both the transceivers' synchronization and bandwidth requirements. Then, a high-precision pulse reconstruction method considering the LED model and impulse response is established to enhance the signal quality. Moreover, the position is estimated based on the maximum a posteriori (MAP) probability criterion. Experimental results obtained by the VLP testbed demonstrate that the proposed scheme outperforms the benchmark positioning schemes. It achieves a positioning accuracy of 1.7 cm by using the reconstructed 2 GHz sampling rate in the case of a bandwidth of 50 MHz and a real sampling rate of 100 MHz. Last but not least, the proposed scheme maintains positioning accuracy within 30 cm even with a few MHz bandwidth of LED.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10356613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081274","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}
Real-time object pose estimation and tracking is challenging but essential for some emerging applications, such as augmented reality. In general, state-of-the-art methods address this problem using deep neural networks, which indeed yield satisfactory results. Nevertheless, the high computational cost of these methods makes them unsuitable for mobile devices where real-world applications usually take place. We propose real-time object pose tracking system with low computational cost for mobile devices. It is a monocular inertial-assisted-visual system with a client–server architecture connected by high-speed networking. Inertial measurement unit (IMU) pose propagation is performed on the client side for fast pose tracking, and RGB image-based 3-D object pose estimation is performed on the server side to obtain accurate poses, after which the pose is sent to the client side for refinement, where we propose a bias self-correction mechanism to reduce the drift. We also propose a fast and effective pose inspection algorithm to detect tracking failures and incorrect pose estimation. In this way, the pose updates rapidly even within 5 ms on low-level devices, making it possible to support real-time tracking for applications. In addition, an object pose dataset with RGB images and IMU measurements is delivered for evaluation. Experiments also show that our method performs well with both accuracy and robustness.
{"title":"Real-Time Object Pose Tracking System With Low Computational Cost for Mobile Devices","authors":"Yo-Chung Lau;Kuan-Wei Tseng;Peng-Yuan Kao;I-Ju Hsieh;Hsiao-Ching Tseng;Yi-Ping Hung","doi":"10.1109/JISPIN.2023.3340987","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3340987","url":null,"abstract":"Real-time object pose estimation and tracking is challenging but essential for some emerging applications, such as augmented reality. In general, state-of-the-art methods address this problem using deep neural networks, which indeed yield satisfactory results. Nevertheless, the high computational cost of these methods makes them unsuitable for mobile devices where real-world applications usually take place. We propose real-time object pose tracking system with low computational cost for mobile devices. It is a monocular inertial-assisted-visual system with a client–server architecture connected by high-speed networking. Inertial measurement unit (IMU) pose propagation is performed on the client side for fast pose tracking, and RGB image-based 3-D object pose estimation is performed on the server side to obtain accurate poses, after which the pose is sent to the client side for refinement, where we propose a bias self-correction mechanism to reduce the drift. We also propose a fast and effective pose inspection algorithm to detect tracking failures and incorrect pose estimation. In this way, the pose updates rapidly even within 5 ms on low-level devices, making it possible to support real-time tracking for applications. In addition, an object pose dataset with RGB images and IMU measurements is delivered for evaluation. Experiments also show that our method performs well with both accuracy and robustness.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"211-220"},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10352604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822151","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 : 2023-12-08DOI: 10.1109/JISPIN.2023.3340638
F. Serhan Daniş
Fingerprinting techniques are known to perform better for radio-frequency-based indoor positioning compared to lateration-based techniques. However, accurate fingerprinting depends on a thorough prior scene analysis, in which the area should be described in terms of the signal parameters the positioning system deploys. This requires a heavy workload to build accurate systems, causing a tradeoff between accuracy and practicality. In this article, we propose a chain of subsequent preprocessing techniques for generating accurate radio frequency maps (RMs). The techniques consist of filtering the received signal strength indicator and interpolating the local probability distribution parameters. The proposed subsequent techniques generate smoother RMs and describe these maps with only two parameters per position. By plugging an adaptive particle filter as the position estimation algorithm, we show that the generated RMs increase the positioning accuracy significantly. We also investigate the relation between practicality and accuracy in terms of the invested time in the process of fingerprinting and the stored data to represent the RM. Alongside the increased accuracy of the proposed system, the approach allows a dramatic increase in the practicality of the fingerprinting technique.
{"title":"Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor Positioning","authors":"F. Serhan Daniş","doi":"10.1109/JISPIN.2023.3340638","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3340638","url":null,"abstract":"Fingerprinting techniques are known to perform better for radio-frequency-based indoor positioning compared to lateration-based techniques. However, accurate fingerprinting depends on a thorough prior scene analysis, in which the area should be described in terms of the signal parameters the positioning system deploys. This requires a heavy workload to build accurate systems, causing a tradeoff between accuracy and practicality. In this article, we propose a chain of subsequent preprocessing techniques for generating accurate radio frequency maps (RMs). The techniques consist of filtering the received signal strength indicator and interpolating the local probability distribution parameters. The proposed subsequent techniques generate smoother RMs and describe these maps with only two parameters per position. By plugging an adaptive particle filter as the position estimation algorithm, we show that the generated RMs increase the positioning accuracy significantly. We also investigate the relation between practicality and accuracy in terms of the invested time in the process of fingerprinting and the stored data to represent the RM. Alongside the increased accuracy of the proposed system, the approach allows a dramatic increase in the practicality of the fingerprinting technique.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"199-210"},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10349912","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822032","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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