IEEE Journal of Indoor and Seamless Positioning and Navigation最新文献_第2页

LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization LuViRA 数据集验证与讨论：比较用于室内定位的视觉、无线电和音频传感器

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-07-16 DOI: 10.1109/JISPIN.2024.3429110

Ilayda Yaman;Guoda Tian;Erik Tegler;Jens Gulin;Nikhil Challa;Fredrik Tufvesson;Ove Edfors;Kalle Åström;Steffen Malkowsky;Liang Liu

{"title":"LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization","authors":"Ilayda Yaman;Guoda Tian;Erik Tegler;Jens Gulin;Nikhil Challa;Fredrik Tufvesson;Ove Edfors;Kalle Åström;Steffen Malkowsky;Liang Liu","doi":"10.1109/JISPIN.2024.3429110","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3429110","url":null,"abstract":"In this article, we present a unique comparative analysis, and evaluation of vision-, radio-, and audio-based localization algorithms. We create the first baseline for the aforementioned sensors using the recently published Lund University Vision, Radio, and Audio dataset, where all the sensors are synchronized and measured in the same environment. Some of the challenges of using each specific sensor for indoor localization tasks are highlighted. Each sensor is paired with a current state-of-the-art localization algorithm and evaluated for different aspects: localization accuracy, reliability and sensitivity to environment changes, calibration requirements, and potential system complexity. Specifically, the evaluation covers the Oriented FAST and Rotated BRIEF simultaneous localization and mapping (SLAM) algorithm for vision-based localization with an RGB-D camera, a machine learning algorithm for radio-based localization with massive multiple-input multiple-output (MIMO) technology, and the StructureFromSound2 algorithm for audio-based localization with distributed microphones. The results can serve as a guideline and basis for further development of robust and high-precision multisensory localization systems, e.g., through sensor fusion, and context- and environment-aware adaptations.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"240-250"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Velocity-Based Channel Charting With Spatial Distribution Map Matching 基于速度的航道制图与空间分布图匹配

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-07-09 DOI: 10.1109/JISPIN.2024.3424768

Maximilian Stahlke;George Yammine;Tobias Feigl;Bjoern M. Eskofier;Christopher Mutschler

{"title":"Velocity-Based Channel Charting With Spatial Distribution Map Matching","authors":"Maximilian Stahlke;George Yammine;Tobias Feigl;Bjoern M. Eskofier;Christopher Mutschler","doi":"10.1109/JISPIN.2024.3424768","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3424768","url":null,"abstract":"Radio fingerprinting (FP) technologies improve localization performance in challenging non-line-of-sight environments. However, FP is expensive as its life cycle management requires recording reference signals for initial training and when the environment changes. Instead, novel channel charting technologies are significantly cheaper. Because they implicitly assign relative coordinates to radio signals, they require few reference coordinates for localization. However, even channel charting still requires data acquisition and reference signals, and its localization is slightly less accurate than FP. In this article, we propose a novel channel charting framework that does not require references and dramatically reduces life-cycle management. With velocity information, e.g., pedestrian dead reckoning or odometry, we model relative charts. And with topological map information, e.g., building floor plans, we transform them into real coordinates. In a large-scale study, we acquired two realistic datasets using 5G and single-input and multiple-output distributed radio systems with noisy velocities and coarse map information. Our experiments show that we achieve the localization accuracy of FP but without reference information.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"230-239"},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10591331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Multipath Component Delays With Transformer Models 利用变压器模型估算多径分量延迟

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-07-03 DOI: 10.1109/JISPIN.2024.3422908

Jonathan Ott;Maximilian Stahlke;Tobias Feigl;Christopher Mutschler

引用次数: 0

UWB Positioning Integrity Estimation Using Ranging Residuals and ML Augmented Filtering 利用测距残差和 ML 增强滤波进行 UWB 定位完整性估计

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-06-24 DOI: 10.1109/JISPIN.2024.3418296

Mihkel Tommingas;Muhammad Mahtab Alam;Ivo Müürsepp;Sander Ulp

{"title":"UWB Positioning Integrity Estimation Using Ranging Residuals and ML Augmented Filtering","authors":"Mihkel Tommingas;Muhammad Mahtab Alam;Ivo Müürsepp;Sander Ulp","doi":"10.1109/JISPIN.2024.3418296","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3418296","url":null,"abstract":"This article investigates the use of ultrawideband (UWB) ranging residuals for coordinate integrity estimation and their use in a filtering scheme. Typically, UWB system accuracy is improved using channel statistics (CSs) to detect and mitigate non-line-of-sight effects between UWB sensors and the object to be located, potentially improving the end coordinate solution. However, in practice, when considering UWB system with a high positioning update rate, this is not a feasible approach, as gathering and processing CS data takes too much time. In contrast to this approach, this article proposes a set of features based on UWB ranging residuals that could be used as an alternative in integrity assessment. By using machine learning (ML), the most important features were extracted from the initial set, and then, used to train and validate a model for UWB coordinate error prediction. Finally, the prediction was applied in an adaptive Kalman filtering scheme as an input for measurement uncertainty. Model testing was done using UWB measurement test dataset gathered at an industrial site. The overall results showed significant improvement in 2-D and 3-D positioning metrics of ML-augmented filtering when compared to non-ML filtering. On average, the end coordinates in the test set had ca. 10 cm smaller mean location error and ca. 40 cm smaller dispersion in 2-D positioning. In addition, the presence of outliers was reduced significantly as the maximum error offset decreased by several meters. Although ML augmented filtering is computationally slower than non-ML filtering (e.g., ordinary and extended Kalman filter), it is still faster than using CS for UWB integrity estimation. The results show that using the proposed residual features in an ML model provides a feasible approach to predict UWB positioning integrity and use it as a measure of uncertainty in a coordinate filtering scheme.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"205-218"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10568925","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Wi-Fi RSS-RTT Indoor Positioning Model Based on Dynamic Model Switching Algorithm 基于动态模型切换算法的 Wi-Fi RSS-RTT 室内定位模型

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-04-05 DOI: 10.1109/JISPIN.2024.3385356

Xu Feng;Khuong An Nguyen;Zhiyuan Luo

引用次数: 0

Ubiquitous UWB Ranging Error Mitigation With Application to Infrastructure-Free Cooperative Positioning 无处不在的 UWB 测距误差缓解技术在无基础设施合作定位中的应用

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-04-03 DOI: 10.1109/JISPIN.2024.3384909

Maija Mäkelä;Martta-Kaisa Olkkonen;Martti Kirkko-Jaakkola;Toni Hammarberg;Tuomo Malkamäki;Jesperi Rantanen;Sanna Kaasalainen

引用次数: 0

Self-Localization Method Using a Single Acoustic Ranging Sensor Based on Impulse Response and Doppler Effect 基于脉冲响应和多普勒效应的单个声学测距传感器自定位方法

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-03-21 DOI: 10.1109/JISPIN.2024.3403519

Atsushi Tsuchiya;Naoto Wakatsuki;Tadashi Ebihara;Keiichi Zempo;Koichi Mizutani

{"title":"Self-Localization Method Using a Single Acoustic Ranging Sensor Based on Impulse Response and Doppler Effect","authors":"Atsushi Tsuchiya;Naoto Wakatsuki;Tadashi Ebihara;Keiichi Zempo;Koichi Mizutani","doi":"10.1109/JISPIN.2024.3403519","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3403519","url":null,"abstract":"This study aims to realize self-position estimation for indoor robots using only a single acoustic channel. When a single omnidirectional transmitter/receiver is used as an object detection sensor, detected objects are identified on concentric circles with the transmitter/receiver as the center point. Self-position estimation method using this sensor cannot use the directional information of the detected object. This fact makes it impossible to specify the robot's turning angle using environmental information. In this article, we propose a self-position estimation method using a single omnidirectional transmitter/receiver that can consider the direction of the reflected object by estimating the direction of the reflected wave from the Doppler effect generated during the robot's movement. The self-position estimation was implemented by using echo images of the direction of arrival of sound waves estimated from the Doppler effect and the distance of arrival of sound waves estimated from the impulse response and matching them with a previously generated map image. The accuracy of the proposed method was evaluated by simulation and experiment. In the simulation, an average position estimation error of 0.042 m was achieved; in the experiment, it was 0.051 m. Furthermore, experimental and simulation results show that using the Doppler effect contributes to self-position estimation accuracy.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"193-204"},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10535727","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Indoor Localization Systems Through a Handoff Protocol 通过切换协议整合室内定位系统

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-03-13 DOI: 10.1109/JISPIN.2024.3377146

Francesco Furfari;Michele Girolami;Paolo Barsocchi

引用次数: 0

Uncertainty-Based Fingerprinting Model Monitoring for Radio Localization 基于不确定性的无线电定位指纹模型监测

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-03-09 DOI: 10.1109/JISPIN.2024.3398568

Maximilian Stahlke;Tobias Feigl;Sebastian Kram;Bjoern M. Eskofier;Christopher Mutschler

{"title":"Uncertainty-Based Fingerprinting Model Monitoring for Radio Localization","authors":"Maximilian Stahlke;Tobias Feigl;Sebastian Kram;Bjoern M. Eskofier;Christopher Mutschler","doi":"10.1109/JISPIN.2024.3398568","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3398568","url":null,"abstract":"Indoor radio environments often consist of areas with mixed propagation conditions. In line-of-sight (LoS)-dominated areas, classic time-of-flight (ToF) methods reliably return accurate positions, while in nonline-of-sight (NLoS) dominated areas (AI-based) fingerprinting methods are required. However, fingerprinting methods are only cost-efficient if they are used exclusively in NLoS-dominated areas due to their expensive life cycle management. Systems that are both accurate and cost-efficient in LoS- and NLoS-dominated areas require identification of those areas to select the optimal localization method. To enable a reliable and robust life cycle management of fingerprinting, we must identify altered fingerprints to trigger update processes. In this article, we propose methods for uncertainty estimation of AI-based fingerprinting to determine its spatial boundaries and validity. Our experiments show that we can successfully identify spatial boundaries of the fingerprinting models and detect corrupted areas. In contrast to the state-of-the-art, our approach employs an intrinsic identification through out-of-distribution (OOD) detection, rendering external detection approaches unnecessary.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"166-176"},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10526425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Contained Pedestrian Navigation Fusing ML-Selected GNSS Carrier Phase and Inertial Signals in Challenging Environments 在挑战性环境中融合经 ML 筛选的 GNSS 载波相位和惯性信号的自给式行人导航系统

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2024-03-06 DOI: 10.1109/JISPIN.2024.3397229

Ziyou Li;Ni Zhu;Valérie Renaudin

{"title":"Self-Contained Pedestrian Navigation Fusing ML-Selected GNSS Carrier Phase and Inertial Signals in Challenging Environments","authors":"Ziyou Li;Ni Zhu;Valérie Renaudin","doi":"10.1109/JISPIN.2024.3397229","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3397229","url":null,"abstract":"The performance of the global navigation satellite system (GNSS)-based navigation is usually degraded in challenging environments, such as deep urban and light indoors. In such environments, the satellite visibility is reduced, and the complex propagation conditions perturb the GNSS signals with attenuation, refraction, and frequent reflection. This article presents a novel artificial intelligence (AI)-based approach, to tackle the complex GNSS positioning problems in deep urban, even light indoors. The new approach, called LIGHT, i.e., Light Indoor GNSS macHine-learning-based Time difference carrier phase, can select healthy GNSS carrier phase data for positioning, thanks to machine learning (ML). The selected carrier phase data are fed into a time difference carrier phase (TDCP)-based extended Kalman filter to estimate the user's velocity. Four trajectories including shopping mall, railway station, shipyard, as well as urban canyon scenarios over a 3.2-km total walking distance with a handheld device are tested. It is shown that at least half of the epochs are selected as usable for light indoor GNSS TDCP standalone positioning, and the accuracy of the velocity estimates can improve up to 88% in terms of the 75\u0000<inline-formula><tex-math>${text{th}}$</tex-math></inline-formula>\u0000 percentile of the absolute horizontal velocity error compared with the state-of-the-art non-ML approach. Furthermore, a newly designed hybridization filter LIGHT-PDR that fuses the LIGHT algorithm and pedestrian dead reckoning solution is applied to perform seamless indoor/outdoor positioning in a more robust manner.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"177-192"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10520899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0