Pub Date : 2026-01-06DOI: 10.1109/JISPIN.2026.3651879
{"title":"2025 Index IEEE Journal of Indoor and Seamless Positioning and Navigation","authors":"","doi":"10.1109/JISPIN.2026.3651879","DOIUrl":"https://doi.org/10.1109/JISPIN.2026.3651879","url":null,"abstract":"","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"295-301"},"PeriodicalIF":0.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929624","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 : 2025-12-18DOI: 10.1109/JISPIN.2025.3645724
Lukas Brechtel;Christoph Fischer;Hans D. Schotten
This article proposes a novel 6G downlink waveform designed for passive coherent location using orthogonal time–frequency space (OTFS) modulation. Targeting private campus networks, the approach enables passive sensing without active signal emission, addressing key challenges of energy consumption, interference, and cost in industrial automation. The OTFS framework operates in the delay–Doppler domain, allowing seamless integration of radar functionality into communication signals while maintaining synchronization-free operation through local signal processing.A comprehensive simulation-based analysis of OTFS grid configurations reveals fundamental tradeoffs between sensing resolution and computational efficiency. Controlled ray-traced simulations support the theoretical framework, indicating high-resolution target detection capabilities that meet third Generation Partnership Project requirements for autonomous mobile robot navigation. The proposed architecture offers power advantages through elimination of transmit amplification, the primary power consumer in active radar systems, and provides inherent privacy advantages through passive operation and distributed processing.Processing chain analysis reveals strong compatibility with multistatic extensions, requiring only evolutionary modifications rather than fundamental redesign.Simulation results suggest the feasibility of dual-use signaling in future 6G networks, with applications extending beyond industrial automation to smart cities, traffic monitoring, and public safety systems.
{"title":"A Passive Coherent Location Friendly 6G for Private Campus Networks","authors":"Lukas Brechtel;Christoph Fischer;Hans D. Schotten","doi":"10.1109/JISPIN.2025.3645724","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3645724","url":null,"abstract":"This article proposes a novel 6G downlink waveform designed for passive coherent location using orthogonal time–frequency space (OTFS) modulation. Targeting private campus networks, the approach enables passive sensing without active signal emission, addressing key challenges of energy consumption, interference, and cost in industrial automation. The OTFS framework operates in the delay–Doppler domain, allowing seamless integration of radar functionality into communication signals while maintaining synchronization-free operation through local signal processing.A comprehensive simulation-based analysis of OTFS grid configurations reveals fundamental tradeoffs between sensing resolution and computational efficiency. Controlled ray-traced simulations support the theoretical framework, indicating high-resolution target detection capabilities that meet third Generation Partnership Project requirements for autonomous mobile robot navigation. The proposed architecture offers power advantages through elimination of transmit amplification, the primary power consumer in active radar systems, and provides inherent privacy advantages through passive operation and distributed processing.Processing chain analysis reveals strong compatibility with multistatic extensions, requiring only evolutionary modifications rather than fundamental redesign.Simulation results suggest the feasibility of dual-use signaling in future 6G networks, with applications extending beyond industrial automation to smart cities, traffic monitoring, and public safety systems.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"11-22"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982229","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}
Multimodal transport refers to multiple transportation means (e.g., car and plane) that can be used to transport people or goods. Classifying the mode of transportation can have multiple usages toward sustainable transport solutions, such as optimizing routes, reducing transit times, having efficient logistics operations, reducing transportation costs by strategically combining different modes, or understanding how people move within cities for migration studies. Multimodal transport classification has traditionally relied on data collected from various movement sensors (e.g., accelerometers, pedometers, and gyroscopes); yet, with the opening of the access to raw global navigation satellite system (GNSS) data on mobile devices, new avenues of multimodal analysis have been created, when GNSS signals alone (without additional sensors) could be used to classify the mode of transport. This article introduces a novel Receiver Independent Exchange (RINEX)-based framework for multimodal transport classification that operates exclusively on instantaneous raw GNSS observables, without relying on position estimates or auxiliary motion sensors. Unlike traditional approaches that require at least four satellites for positioning, the proposed method achieves classification using data from as little as one strongest satellite in view. By leveraging machine learning algorithms, transportation modes are inferred directly from single and double differences of pseudorange, Doppler, and carrier-to-noise ratio features extracted from raw RINEX data. The framework was validated using an extensive dataset collected from 18 volunteers across five European countries, using 409 tracks and ten transportation modes. The results show that accurate and stable classification is possible even with limited satellite visibility, demonstrating the feasibility of low-power, privacy-preserving, and geometry-aware mobility analytics based solely on raw GNSS measurements.
{"title":"Toward Understanding Multimodal Transport Classification Using Features From RINEX Data Extracted From Android Phones","authors":"Yelyzaveta Pervysheva;Jari Nurmi;Elena Simona Lohan","doi":"10.1109/JISPIN.2025.3644838","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3644838","url":null,"abstract":"Multimodal transport refers to multiple transportation means (e.g., car and plane) that can be used to transport people or goods. Classifying the mode of transportation can have multiple usages toward sustainable transport solutions, such as optimizing routes, reducing transit times, having efficient logistics operations, reducing transportation costs by strategically combining different modes, or understanding how people move within cities for migration studies. Multimodal transport classification has traditionally relied on data collected from various movement sensors (e.g., accelerometers, pedometers, and gyroscopes); yet, with the opening of the access to raw global navigation satellite system (GNSS) data on mobile devices, new avenues of multimodal analysis have been created, when GNSS signals alone (without additional sensors) could be used to classify the mode of transport. This article introduces a novel Receiver Independent Exchange (RINEX)-based framework for multimodal transport classification that operates exclusively on instantaneous raw GNSS observables, without relying on position estimates or auxiliary motion sensors. Unlike traditional approaches that require at least four satellites for positioning, the proposed method achieves classification using data from as little as one strongest satellite in view. By leveraging machine learning algorithms, transportation modes are inferred directly from single and double differences of pseudorange, Doppler, and carrier-to-noise ratio features extracted from raw RINEX data. The framework was validated using an extensive dataset collected from 18 volunteers across five European countries, using 409 tracks and ten transportation modes. The results show that accurate and stable classification is possible even with limited satellite visibility, demonstrating the feasibility of low-power, privacy-preserving, and geometry-aware mobility analytics based solely on raw GNSS measurements.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301594","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861210","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 : 2025-12-05DOI: 10.1109/JISPIN.2025.3640563
Christophe Villien;Lélio Chetot;Jules Burgat
Applications such as drone inspection frequently rely on ultrawideband (UWB) for positioning in environments where global navigation satellite system is unavailable or unreliable. In some cases, an additional radar system is employed to detect obstacles or monitor the distance to a target object. In this article, a novel localization approach that eliminates the need for a dedicated radar system is studied. It relies on the channel impulse response obtained from radar measurements of cost-effective embedded UWB devices. Unlike prior methods, radar measurements are conducted from a moving platform, which makes background clutter removal particularly challenging. Specific radar echoes are identified, associated with known obstacles, and then fused with UWB distance measurements to enhance positioning accuracy. When real-time positioning is not required, a new postprocessing algorithm based on multiple hypothesis tracking (MHT) and two-filter smoothing (TFS) is proposed. Compared to traditional MHT, it features reduced complexity in data association. Field experiments demonstrate that the proposed method achieves radar-based distance measurement accuracy of 6.5 cm. In real-time scenarios, horizontal and vertical positioning errors are reduced from 75 and 189 cm (UWB only) to 53 and 65 cm, respectively, when radar measurements are integrated. In offline processing scenarios, TFS-MHT further reduces these errors to 32 cm horizontally and 39 cm vertically, demonstrating the efficiency of the approach.
{"title":"Radar-Aided Localization Using CIR From UWB Devices With Two-Filter Smoothing MHT","authors":"Christophe Villien;Lélio Chetot;Jules Burgat","doi":"10.1109/JISPIN.2025.3640563","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3640563","url":null,"abstract":"Applications such as drone inspection frequently rely on ultrawideband (UWB) for positioning in environments where global navigation satellite system is unavailable or unreliable. In some cases, an additional radar system is employed to detect obstacles or monitor the distance to a target object. In this article, a novel localization approach that eliminates the need for a dedicated radar system is studied. It relies on the channel impulse response obtained from radar measurements of cost-effective embedded UWB devices. Unlike prior methods, radar measurements are conducted from a moving platform, which makes background clutter removal particularly challenging. Specific radar echoes are identified, associated with known obstacles, and then fused with UWB distance measurements to enhance positioning accuracy. When real-time positioning is not required, a new postprocessing algorithm based on multiple hypothesis tracking (MHT) and two-filter smoothing (TFS) is proposed. Compared to traditional MHT, it features reduced complexity in data association. Field experiments demonstrate that the proposed method achieves radar-based distance measurement accuracy of 6.5 cm. In real-time scenarios, horizontal and vertical positioning errors are reduced from 75 and 189 cm (UWB only) to 53 and 65 cm, respectively, when radar measurements are integrated. In offline processing scenarios, TFS-MHT further reduces these errors to 32 cm horizontally and 39 cm vertically, demonstrating the efficiency of the approach.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"280-294"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778234","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 : 2025-12-05DOI: 10.1109/JISPIN.2025.3640562
Yih-Shyh Chiou;Yang-Ke Lin;Chun-Yi Chou;Tsung-Hsuan Chen;You-Sheng Zhang;Yu-Jhih Chen;Yi-Hsuan Liu
With the rapid development of positioning, localization, navigation, and self-driving car systems, the implementation of intelligent and robust localization systems for real-time location-based services (LBSs) has become increasingly attractive. This article presents high-performance positioning and tracking approaches characterized by a pipelined structure, high computational efficiency, flexibility, and real-time processing, implemented using field programmable gate arrays (FPGAs). In triangulation-based positioning approaches, estimated distance information is derived from communication signals and the path loss model, while vertical localization is achieved through the characteristics of barometric pressure (BP). After integrating positioning approaches with tracking methods and BP sensors, the results illustrate that the proposed localization algorithms closely estimate the trajectory of mobile devices. For FPGA-implemented algorithms, the proposed approaches effectively handle floating-point operations, reduce computing resource usage, and provide real-time processing capabilities, surpassing software-based designs and implementations. In terms of performance, the results demonstrate that the localization accuracy of the proposed hardware-based implementation is nearly identical to that of the software-based approach. Regarding vertical location accuracy, based on the proposed calibration approach, the BP value increases by 11.6 Pa for every one-meter decrease in altitude. To maintain floor-level accuracy over time despite atmospheric fluctuations, a real-time dynamic calibration mechanism using a fixed reference sensor is employed. In summary, the proposed localization algorithms, implemented with FPGAs and BP sensors, offer advantages such as lower circuit costs, higher processing efficiency, and reliable vertical location accuracy for real-time public safety LBS.
{"title":"Localization Algorithms Using Tracking Approaches and Barometric Pressure Sensors in Indoor Environments","authors":"Yih-Shyh Chiou;Yang-Ke Lin;Chun-Yi Chou;Tsung-Hsuan Chen;You-Sheng Zhang;Yu-Jhih Chen;Yi-Hsuan Liu","doi":"10.1109/JISPIN.2025.3640562","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3640562","url":null,"abstract":"With the rapid development of positioning, localization, navigation, and self-driving car systems, the implementation of intelligent and robust localization systems for real-time location-based services (LBSs) has become increasingly attractive. This article presents high-performance positioning and tracking approaches characterized by a pipelined structure, high computational efficiency, flexibility, and real-time processing, implemented using field programmable gate arrays (FPGAs). In triangulation-based positioning approaches, estimated distance information is derived from communication signals and the path loss model, while vertical localization is achieved through the characteristics of barometric pressure (BP). After integrating positioning approaches with tracking methods and BP sensors, the results illustrate that the proposed localization algorithms closely estimate the trajectory of mobile devices. For FPGA-implemented algorithms, the proposed approaches effectively handle floating-point operations, reduce computing resource usage, and provide real-time processing capabilities, surpassing software-based designs and implementations. In terms of performance, the results demonstrate that the localization accuracy of the proposed hardware-based implementation is nearly identical to that of the software-based approach. Regarding vertical location accuracy, based on the proposed calibration approach, the BP value increases by 11.6 Pa for every one-meter decrease in altitude. To maintain floor-level accuracy over time despite atmospheric fluctuations, a real-time dynamic calibration mechanism using a fixed reference sensor is employed. In summary, the proposed localization algorithms, implemented with FPGAs and BP sensors, offer advantages such as lower circuit costs, higher processing efficiency, and reliable vertical location accuracy for real-time public safety LBS.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"260-279"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778201","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 : 2025-11-06DOI: 10.1109/JISPIN.2025.3629705
Valerie Renaudin;Mohamad Issam Sayyaf;Frédéric Le Bourhis;Miguel Ortiz
What began with isolated incidents of GPS interference has grown into a global crisis that threatens everything from commercial aviation to military operations. This article documents the alarming reality and the importance of Global Navigation Satellite System (GNSS)-denied navigation technologies in this context. GNSS attacks have increased sevenfold in contested regions, rendering precision-guided weapons nearly useless and forcing airlines to abandon entire routes. The availability of some inexpensive jammers (less than ${$}$50) has meant that the devices used by countries can be easily acquired by anyone, significantly compromising security. We present an investigation of this escalating threat through numerous comprehensive real-world case studies, including air traffic chaos over the Baltic Sea, maritime spoofing in international waters and the failure of precision weapons in active conflict zones. In addition, we share an 819-minute open-source dataset of experimental GNSS raw data, featuring three different types of attacks on a GNSS receiver under various motion conditions. The analysis of the main impact of these attacks on the raw measurements at the receiver level and a summary of the footprint of each attack based on the measurements is also provided. Finally we explain how the positioning and navigation solutions developed for indoors offer decisive advantages for mitigating these attacks, solving outdoor navigation vulnerabilities. This research shows that the future of secure navigation lies not in hardening satellite systems, but in making them optional.
{"title":"GNSS Positioning Under Threat: The Rising Risk to Existing Systems and The Role of Alternative Indoor and Seamless Navigation Technologies","authors":"Valerie Renaudin;Mohamad Issam Sayyaf;Frédéric Le Bourhis;Miguel Ortiz","doi":"10.1109/JISPIN.2025.3629705","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3629705","url":null,"abstract":"What began with isolated incidents of GPS interference has grown into a global crisis that threatens everything from commercial aviation to military operations. This article documents the alarming reality and the importance of Global Navigation Satellite System (GNSS)-denied navigation technologies in this context. GNSS attacks have increased sevenfold in contested regions, rendering precision-guided weapons nearly useless and forcing airlines to abandon entire routes. The availability of some inexpensive jammers (less than <inline-formula><tex-math>${$}$</tex-math></inline-formula>50) has meant that the devices used by countries can be easily acquired by anyone, significantly compromising security. We present an investigation of this escalating threat through numerous comprehensive real-world case studies, including air traffic chaos over the Baltic Sea, maritime spoofing in international waters and the failure of precision weapons in active conflict zones. In addition, we share an 819-minute open-source dataset of experimental GNSS raw data, featuring three different types of attacks on a GNSS receiver under various motion conditions. The analysis of the main impact of these attacks on the raw measurements at the receiver level and a summary of the footprint of each attack based on the measurements is also provided. Finally we explain how the positioning and navigation solutions developed for indoors offer decisive advantages for mitigating these attacks, solving outdoor navigation vulnerabilities. This research shows that the future of secure navigation lies not in hardening satellite systems, but in making them optional.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"246-259"},"PeriodicalIF":0.0,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11230561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674790","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 : 2025-10-28DOI: 10.1109/JISPIN.2025.3622210
Daan Delabie;Thomas Feys;Chesney Buyle;Bert Cox;Liesbet Van der Perre;Lieven De Strycker
In [1], reference [30] is added and provided as follows.
在[1]中增加参考[30]，如下所示。
{"title":"Corrections to “Echoes of Accuracy: Enhancing Ultrasonic Indoor Positioning for Energy-Neutral Devices With Neural Network Approaches”","authors":"Daan Delabie;Thomas Feys;Chesney Buyle;Bert Cox;Liesbet Van der Perre;Lieven De Strycker","doi":"10.1109/JISPIN.2025.3622210","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3622210","url":null,"abstract":"In [1], reference [30] is added and provided as follows.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"245-245"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11220183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405382","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 : 2025-08-13DOI: 10.1109/JISPIN.2025.3598688
Daan Delabie;Thomas Feys;Chesney Buyle;Bert Cox;Liesbet Van der Perre;Lieven De Strycker
With increasing interest in indoor positioning systems across various domains, such as industry, retail, and healthcare, the search for optimal solutions to meet the needs of different applications has gained significant momentum. This work highlights the potential of hybrid RF-acoustic systems combined with advanced machine learning models for robust, scalable, and energy-efficient indoor localization. The focus is on enhancing positioning algorithms for energy-neutral devices to improve accuracy, precision, reliability, and ease of installation. Traditional model-based (MB) methods, relying on line-of-sight (LoS) components, often struggle in challenging nonline-of-sight (NLoS) and reverberant environments. To address this, we propose data-driven neural network (NN) approaches capable of harnessing multipath components (MPCs) as additional information. The echoes in the room are exploited to improve accuracy. Various NN architectures, including multilayer perceptrons, (circular) convolutional neural networks, and graph neural networks (GNNs) are evaluated, in first instance using synthetic data. Results demonstrate that especially GNNs outperform MB methods, achieving superior accuracy in both LoS and NLoS scenarios. During the second phase, extensive real-life experiments are carried out. The GNN is evaluated using cross-validation, training on measurement data, and transfer learning (TL) within a reverberant NLoS environment. The cross-validation and TL demonstrate the practical feasibility. We report over 80% of improvement in 3-D positioning error compared to the MB technique.
{"title":"Echoes of Accuracy: Enhancing Ultrasonic Indoor Positioning for Energy-Neutral Devices With Neural Network Approaches","authors":"Daan Delabie;Thomas Feys;Chesney Buyle;Bert Cox;Liesbet Van der Perre;Lieven De Strycker","doi":"10.1109/JISPIN.2025.3598688","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3598688","url":null,"abstract":"With increasing interest in indoor positioning systems across various domains, such as industry, retail, and healthcare, the search for optimal solutions to meet the needs of different applications has gained significant momentum. This work highlights the potential of hybrid RF-acoustic systems combined with advanced machine learning models for robust, scalable, and energy-efficient indoor localization. The focus is on enhancing positioning algorithms for energy-neutral devices to improve accuracy, precision, reliability, and ease of installation. Traditional model-based (MB) methods, relying on line-of-sight (LoS) components, often struggle in challenging nonline-of-sight (NLoS) and reverberant environments. To address this, we propose data-driven neural network (NN) approaches capable of harnessing multipath components (MPCs) as additional information. The echoes in the room are exploited to improve accuracy. Various NN architectures, including multilayer perceptrons, (circular) convolutional neural networks, and graph neural networks (GNNs) are evaluated, in first instance using synthetic data. Results demonstrate that especially GNNs outperform MB methods, achieving superior accuracy in both LoS and NLoS scenarios. During the second phase, extensive real-life experiments are carried out. The GNN is evaluated using cross-validation, training on measurement data, and transfer learning (TL) within a reverberant NLoS environment. The cross-validation and TL demonstrate the practical feasibility. We report over 80% of improvement in 3-D positioning error compared to the MB technique.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"227-244"},"PeriodicalIF":0.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11124402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926908","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 : 2025-07-16DOI: 10.1109/JISPIN.2025.3589958
Marco Piavanini;Mattia Brambilla;Monica Nicoli
Internet of Things wireless technologies serve as key enabler for location-based services in emerging applications, such as autonomous robotics, industrial automation, augmented reality, and virtual reality. Wideband technologies, including ultra wideband (UWB) and 5G-advanced millimeter-waves, are the preferred solutions in these contexts for their high potentials in precise positioning. A main challenge is the mitigation of radio propagation effects that arise in complex environments, such as in industrial facilities, where frequent blockage events limit the accuracy and integrity of localization services. This article tackles the problem focusing on precise indoor navigation in industrial environments with dense and dynamic blockage conditions. Our proposal relies on an innovative particle filtering technique, based on the Stein variational adaptive importance sampling, to improve the sampled representation of the location posterior distribution by integrating prior information on the intermittent visibility-blockage dynamics. We assess the proposed solution through indoor experiments conducted in industrial scenarios using UWB devices. Our results show significant improvements with respect to state-of-the-art filters in terms of both accuracy and robustness of the location tracking.
{"title":"Switching Model Stein Variational Sampling Filter for Mixed LOS/NLOS Industrial Indoor Positioning","authors":"Marco Piavanini;Mattia Brambilla;Monica Nicoli","doi":"10.1109/JISPIN.2025.3589958","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3589958","url":null,"abstract":"Internet of Things wireless technologies serve as key enabler for location-based services in emerging applications, such as autonomous robotics, industrial automation, augmented reality, and virtual reality. Wideband technologies, including ultra wideband (UWB) and 5G-advanced millimeter-waves, are the preferred solutions in these contexts for their high potentials in precise positioning. A main challenge is the mitigation of radio propagation effects that arise in complex environments, such as in industrial facilities, where frequent blockage events limit the accuracy and integrity of localization services. This article tackles the problem focusing on precise indoor navigation in industrial environments with dense and dynamic blockage conditions. Our proposal relies on an innovative particle filtering technique, based on the Stein variational adaptive importance sampling, to improve the sampled representation of the location posterior distribution by integrating prior information on the intermittent visibility-blockage dynamics. We assess the proposed solution through indoor experiments conducted in industrial scenarios using UWB devices. Our results show significant improvements with respect to state-of-the-art filters in terms of both accuracy and robustness of the location tracking.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"215-226"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11081429","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758154","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 : 2025-06-09DOI: 10.1109/JISPIN.2025.3577979
Majed Ramzi Imad;Jani Käppi;Elena Simona Lohan;Jari Nurmi;Jari Syrjärinne
This article proposes a new model based on supervised machine learning designed for global total electron content (TEC) prediction without relying on atmospheric or solar parameters. The model uses a feedforward neural network (FFNN) with two hidden layers, giving it low complexity and computational cost. By leveraging machine-learning techniques, this model improves a previously established data-driven model proposed by the authors. Our model is trained using TEC data from solar cycle 23, solar cycle 24, and different combinations of both solar cycles. The model is then tested with global ionospheric maps from the $25text{th}$ solar cycle, which were obtained from the International GNSS Service (IGS) database. Our model is also tested with TEC data from the Madrigal database over specific locations and on days with different solar activity levels. The International Reference Ionosphere (IRI) model was used as a benchmark to our model throughout these tests. The results prove that training with data from concatenated solar cycles yields the best performance. When tested with IGS data, our model achieved an average mean absolute error (MAE) of $5.33$ TEC units, which is nearly 15.5% less than what IRI achieved. When compared with data from Madrigal, the model achieved an average MAE of 3.9, 7.1, and 19.9 TEC units on days with quiet, active, and extreme solar activities, respectively. In contrast, the IRI model achieved an average MAE of 5.4, 8, and 15.5 for the same days. Remarkably, our new model has a size of only 36 $mathrm{k}$B, representing over a 1800-fold reduction in size compared to the original data-driven model. Consequently, our proposed model can be regarded as a simple and robust yet precise and independent global TEC model.
{"title":"Reconstruction of an Independent Data-Driven TEC Model Using Machine Learning","authors":"Majed Ramzi Imad;Jani Käppi;Elena Simona Lohan;Jari Nurmi;Jari Syrjärinne","doi":"10.1109/JISPIN.2025.3577979","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3577979","url":null,"abstract":"This article proposes a new model based on supervised machine learning designed for global total electron content (TEC) prediction without relying on atmospheric or solar parameters. The model uses a feedforward neural network (FFNN) with two hidden layers, giving it low complexity and computational cost. By leveraging machine-learning techniques, this model improves a previously established data-driven model proposed by the authors. Our model is trained using TEC data from solar cycle 23, solar cycle 24, and different combinations of both solar cycles. The model is then tested with global ionospheric maps from the <inline-formula><tex-math>$25text{th}$</tex-math></inline-formula> solar cycle, which were obtained from the International GNSS Service (IGS) database. Our model is also tested with TEC data from the Madrigal database over specific locations and on days with different solar activity levels. The International Reference Ionosphere (IRI) model was used as a benchmark to our model throughout these tests. The results prove that training with data from concatenated solar cycles yields the best performance. When tested with IGS data, our model achieved an average mean absolute error (MAE) of <inline-formula><tex-math>$5.33$</tex-math></inline-formula> TEC units, which is nearly 15.5% less than what IRI achieved. When compared with data from Madrigal, the model achieved an average MAE of 3.9, 7.1, and 19.9 TEC units on days with quiet, active, and extreme solar activities, respectively. In contrast, the IRI model achieved an average MAE of 5.4, 8, and 15.5 for the same days. Remarkably, our new model has a size of only 36 <inline-formula><tex-math>$mathrm{k}$</tex-math></inline-formula>B, representing over a 1800-fold reduction in size compared to the original data-driven model. Consequently, our proposed model can be regarded as a simple and robust yet precise and independent global TEC model.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"205-214"},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11028966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581590","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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