Pub Date : 2026-01-19DOI: 10.1109/JISPIN.2026.3655630
Lucas Heublein;Tobias Feigl;Alexander Rügamer;Christopher Mutschler;Felix Ott
Distributed learning and Edge Artificial Intelligence (AI) necessitate efficient data processing, low-latency communication, decentralized model training, and stringent data privacy to facilitate real-time intelligence on edge devices while reducing dependency on centralized infrastructure and ensuring high model performance. In the context of Global Navigation Satellite System (GNSS) applications, the primary objective is to accurately monitor and classify interferences that degrade system performance in distributed environments, thereby enhancing situational awareness. To achieve this, machine learning (ML) models can be deployed on low-resource devices, ensuring minimal communication latency and preserving data privacy. The key challenge is to compress ML models while maintaining high classification accuracy. In this article, we propose variational autoencoders (VAEs) for disentanglement to extract essential latent features that enable accurate classification of interferences. We demonstrate that the disentanglement approach can be leveraged for both data compression and data augmentation by interpolating the lower-dimensional latent representations of signal power. To validate our approach, we evaluate three VAE variants—vanilla, factorized, and conditional generative—and benchmark 19 state-of-the-art VAE and generative models on five distinct datasets, including three collected in controlled indoor environments and two real-world highway datasets. In addition, we conduct extensive hyperparameter searches to optimize performance. Our proposed VAE achieves a data compression rate ranging from 512 to 8192 and achieves an accuracy up to 99.92%. Quantizing our model from float32 to int8 results in a fourfold reduction in model weight size.
{"title":"Variational and Generative Models With Quantization for Disentanglement and Compressed Sensing of GNSS Spectrograms","authors":"Lucas Heublein;Tobias Feigl;Alexander Rügamer;Christopher Mutschler;Felix Ott","doi":"10.1109/JISPIN.2026.3655630","DOIUrl":"https://doi.org/10.1109/JISPIN.2026.3655630","url":null,"abstract":"Distributed learning and Edge Artificial Intelligence (AI) necessitate efficient data processing, low-latency communication, decentralized model training, and stringent data privacy to facilitate real-time intelligence on edge devices while reducing dependency on centralized infrastructure and ensuring high model performance. In the context of Global Navigation Satellite System (GNSS) applications, the primary objective is to accurately monitor and classify interferences that degrade system performance in distributed environments, thereby enhancing situational awareness. To achieve this, machine learning (ML) models can be deployed on low-resource devices, ensuring minimal communication latency and preserving data privacy. The key challenge is to compress ML models while maintaining high classification accuracy. In this article, we propose variational autoencoders (VAEs) for disentanglement to extract essential latent features that enable accurate classification of interferences. We demonstrate that the disentanglement approach can be leveraged for both data compression and data augmentation by interpolating the lower-dimensional latent representations of signal power. To validate our approach, we evaluate three VAE variants—vanilla, factorized, and conditional generative—and benchmark 19 state-of-the-art VAE and generative models on five distinct datasets, including three collected in controlled indoor environments and two real-world highway datasets. In addition, we conduct extensive hyperparameter searches to optimize performance. Our proposed VAE achieves a data compression rate ranging from 512 to 8192 and achieves an accuracy up to 99.92%. Quantizing our model from float32 to int8 results in a fourfold reduction in model weight size.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"65-81"},"PeriodicalIF":0.0,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358953","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175899","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 : 2026-01-16DOI: 10.1109/JISPIN.2026.3654901
Ofir Kruzel;Zeev Yampolsky;Itzik Klein
Inertial sensors are widely used for pedestrian activity recognition. Recent advances in deep learning techniques have significantly improved the inertial classification task’s performance and robustness. However, a standardized benchmark for evaluating and comparing these methods remains lacking. Such a benchmark is critical for ensuring fair and consistent evaluation and future development. In this study, we aim to fill this gap by defining and analyzing 11 data-driven techniques designed to enhance neural inertial classification networks. Our investigation focuses on three key components: network architecture, data augmentation, and data preprocessing. In addition, we conduct comparative analyses to identify the optimal window size for each dataset. This is a parameter that substantially affects model performance but is often overlooked. The experiments were conducted across seven datasets collected from 229 participants and with a total of 4482 min. Among the evaluated techniques, data augmentation through rotation and multihead network architectures yielded the most consistent performance improvements. Our experimental results show that rotation-based augmentation and multihead architectures consistently yield the highest gains, improving accuracy by up to 9.72% depending on the dataset and window length. We additionally quantify the effect of temporal window size, demonstrating that longer segments (2 s) provide the largest average improvement, whereas shorter windows better suit real-time deployment. Finally, we propose a benchmarking strategy to support the future development and evaluation of deep learning models for inertial activity recognition.
{"title":"Optimizing Neural Inertial Classification: A Benchmark Study of Data-Driven Techniques","authors":"Ofir Kruzel;Zeev Yampolsky;Itzik Klein","doi":"10.1109/JISPIN.2026.3654901","DOIUrl":"https://doi.org/10.1109/JISPIN.2026.3654901","url":null,"abstract":"Inertial sensors are widely used for pedestrian activity recognition. Recent advances in deep learning techniques have significantly improved the inertial classification task’s performance and robustness. However, a standardized benchmark for evaluating and comparing these methods remains lacking. Such a benchmark is critical for ensuring fair and consistent evaluation and future development. In this study, we aim to fill this gap by defining and analyzing 11 data-driven techniques designed to enhance neural inertial classification networks. Our investigation focuses on three key components: network architecture, data augmentation, and data preprocessing. In addition, we conduct comparative analyses to identify the optimal window size for each dataset. This is a parameter that substantially affects model performance but is often overlooked. The experiments were conducted across seven datasets collected from 229 participants and with a total of 4482 min. Among the evaluated techniques, data augmentation through rotation and multihead network architectures yielded the most consistent performance improvements. Our experimental results show that rotation-based augmentation and multihead architectures consistently yield the highest gains, improving accuracy by up to 9.72% depending on the dataset and window length. We additionally quantify the effect of temporal window size, demonstrating that longer segments (2 s) provide the largest average improvement, whereas shorter windows better suit real-time deployment. Finally, we propose a benchmarking strategy to support the future development and evaluation of deep learning models for inertial activity recognition.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"53-64"},"PeriodicalIF":0.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11355759","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175981","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 : 2026-01-12DOI: 10.1109/JISPIN.2025.3648680
Nisha L. Raichur;Lucas Heublein;Jaspar Pahl;Dominik Seuß;Christopher Mutschler;Felix Ott
Indoor positioning based on 5G data has achieved high accuracy through the adoption of recent machine learning (ML) techniques. However, the performance of learning-based methods degrades significantly when environmental conditions change, thereby hindering their applicability to new scenarios. Acquiring new training data for each environmental change and fine-tuning ML models is both time-consuming and resource-intensive. This article introduces a domain incremental learning (DIL) approach for dynamic 5G indoor localization, called 5G-DIL, enabling rapid adaptation to environmental changes. We present a novel similarity-aware sampling technique based on the Chebyshev distance, designed to efficiently select specific exemplars from the previous environment while training only on the modified regions of the new environment. This avoids the need to train on the entire region, significantly reducing the time and resources required for adaptation without compromising localization accuracy. This approach requires as few as 50 exemplars from adaptation domains, significantly reducing training time while maintaining high positioning accuracy in previous environments. Comparative evaluations against state-of-the-art DIL techniques on a challenging real-world indoor dataset demonstrate the effectiveness of the proposed sample selection method. Our approach is adaptable to real-world nonline-of-sight propagation scenarios and achieves an mean absolute error positioning error of 0.261 m, even under dynamic environmental conditions.
{"title":"Adaptive and Efficient Sample Selection for Domain Incremental 5G Indoor Localization in Dynamic Multipath Environments","authors":"Nisha L. Raichur;Lucas Heublein;Jaspar Pahl;Dominik Seuß;Christopher Mutschler;Felix Ott","doi":"10.1109/JISPIN.2025.3648680","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3648680","url":null,"abstract":"Indoor positioning based on 5G data has achieved high accuracy through the adoption of recent machine learning (ML) techniques. However, the performance of learning-based methods degrades significantly when environmental conditions change, thereby hindering their applicability to new scenarios. Acquiring new training data for each environmental change and fine-tuning ML models is both time-consuming and resource-intensive. This article introduces a domain incremental learning (DIL) approach for dynamic 5G indoor localization, called 5G-DIL, enabling rapid adaptation to environmental changes. We present a novel similarity-aware sampling technique based on the Chebyshev distance, designed to efficiently select specific exemplars from the previous environment while training only on the modified regions of the new environment. This avoids the need to train on the entire region, significantly reducing the time and resources required for adaptation without compromising localization accuracy. This approach requires as few as 50 exemplars from adaptation domains, significantly reducing training time while maintaining high positioning accuracy in previous environments. Comparative evaluations against state-of-the-art DIL techniques on a challenging real-world indoor dataset demonstrate the effectiveness of the proposed sample selection method. Our approach is adaptable to real-world nonline-of-sight propagation scenarios and achieves an mean absolute error positioning error of 0.261 m, even under dynamic environmental conditions.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"23-40"},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082158","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}
While Wi-Fi positioning is still more common indoors, using magnetic field features has become widely known and utilized as an alternative or supporting source of information. Magnetometer bias presents a significant challenge in magnetic field navigation and simultaneous localization and mapping (SLAM). Traditionally, magnetometers have been calibrated using standard sphere or ellipsoid fitting methods and by requiring manual user procedures, such as rotating a smartphone in a figure-eight shape. This is not always feasible, particularly when the magnetometer is attached to heavy or fast-moving platforms, or when user behavior cannot be reliably controlled. Recent research has proposed using map data for calibration during positioning. This article takes a step further and verifies that a precollected map is not needed; instead, calibration can be done as part of a SLAM process. The presented solution uses a factorized particle filter that factors out calibration in addition to the magnetic field map. The method is validated both indoors and outdoors by using smartphone data from a shopping mall, a university campus building, and an urban outdoor block, alongside mobile robotics data from an office and apartment building environments. Results support the claim that magnetometer calibration can be achieved during SLAM with comparable accuracy to manual calibration. This is supported by analyzing the positioning performance and map consistency, both of which produce results that are almost identical to the rotation-based calibration reported by the operating system.
{"title":"Saying Goodbyes to Rotating Your Phone: Magnetometer Calibration During SLAM—Extended Version","authors":"Ilari Vallivaara;Yinhuan Dong;Bingnan Duan;Tughrul Arslan","doi":"10.1109/JISPIN.2026.3651436","DOIUrl":"https://doi.org/10.1109/JISPIN.2026.3651436","url":null,"abstract":"While Wi-Fi positioning is still more common indoors, using magnetic field features has become widely known and utilized as an alternative or supporting source of information. Magnetometer bias presents a significant challenge in magnetic field navigation and simultaneous localization and mapping (SLAM). Traditionally, magnetometers have been calibrated using standard sphere or ellipsoid fitting methods and by requiring manual user procedures, such as rotating a smartphone in a figure-eight shape. This is not always feasible, particularly when the magnetometer is attached to heavy or fast-moving platforms, or when user behavior cannot be reliably controlled. Recent research has proposed using map data for calibration during positioning. This article takes a step further and verifies that a precollected map is not needed; instead, calibration can be done as part of a SLAM process. The presented solution uses a factorized particle filter that factors out calibration in addition to the magnetic field map. The method is validated both indoors and outdoors by using smartphone data from a shopping mall, a university campus building, and an urban outdoor block, alongside mobile robotics data from an office and apartment building environments. Results support the claim that magnetometer calibration can be achieved during SLAM with comparable accuracy to manual calibration. This is supported by analyzing the positioning performance and map consistency, both of which produce results that are almost identical to the rotation-based calibration reported by the operating system.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"41-52"},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11339882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082171","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 : 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.
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