IEEE Journal of Indoor and Seamless Positioning and Navigation最新文献

Survey-Free Indoor Localization via Cooperative RSS and Learning-Based DOP Estimation 基于协同RSS和基于学习的DOP估计的无测量室内定位

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-02-03 DOI: 10.1109/JISPIN.2026.3660792

Mohamed M. Atia;Soroush Sheikhpour

{"title":"Survey-Free Indoor Localization via Cooperative RSS and Learning-Based DOP Estimation","authors":"Mohamed M. Atia;Soroush Sheikhpour","doi":"10.1109/JISPIN.2026.3660792","DOIUrl":"https://doi.org/10.1109/JISPIN.2026.3660792","url":null,"abstract":"This article presents an automated wireless indoor localization system that eliminates the need for site surveying and reduces the dependency on complete anchors’ location knowledge. The proposed approach leverages cooperative received signal strength (RSS) measurements and a hybrid logarithmic path-loss model to automatically localize network anchors and build radio maps that can be used to localize user nodes. Starting from a limited initial number of known anchor nodes, position estimates are propagated to the rest of the network using an iterative strategy guided by a learning-based dilution of precision (DOP) metric. To address the challenge of unknown anchor locations at initialization, a novel Gaussian process regression (GPR)-based method is introduced to estimate the DOP values without requiring ground-truth coordinates. This enables a self-organizing deployment process that adapts to varying indoor deployments. The system is validated through Cramér–Rao lower bound analysis, simulations, and real-world experiments in ZigBee-based networks. Results demonstrate a root-mean-square localization error of 3 m in a typical indoor 15 m × 30 m office environment covered by seven partially known anchors without prior site survey using only RSS measurements, providing a balance between cost, deployment complexity, and localization accuracy.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"82-95"},"PeriodicalIF":0.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370837","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362501","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

Variational and Generative Models With Quantization for Disentanglement and Compressed Sensing of GNSS Spectrograms GNSS频谱图解纠缠和压缩感知的量化变分和生成模型

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-01-19 DOI: 10.1109/JISPIN.2026.3655630

Lucas Heublein;Tobias Feigl;Alexander Rügamer;Christopher Mutschler;Felix Ott

{"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}

引用次数: 0

Optimizing Neural Inertial Classification: A Benchmark Study of Data-Driven Techniques 优化神经惯性分类：数据驱动技术的基准研究

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-01-16 DOI: 10.1109/JISPIN.2026.3654901

Ofir Kruzel;Zeev Yampolsky;Itzik Klein

{"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}

引用次数: 0

Adaptive and Efficient Sample Selection for Domain Incremental 5G Indoor Localization in Dynamic Multipath Environments 动态多径环境下域增量5G室内定位的自适应高效样本选择

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-01-12 DOI: 10.1109/JISPIN.2025.3648680

Nisha L. Raichur;Lucas Heublein;Jaspar Pahl;Dominik Seuß;Christopher Mutschler;Felix Ott

{"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}

引用次数: 0

Saying Goodbyes to Rotating Your Phone: Magnetometer Calibration During SLAM—Extended Version 告别旋转你的手机：磁力计校准期间摔扩展版本

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-01-12 DOI: 10.1109/JISPIN.2026.3651436

Ilari Vallivaara;Yinhuan Dong;Bingnan Duan;Tughrul Arslan

{"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}

引用次数: 0

2025 Index IEEE Journal of Indoor and Seamless Positioning and Navigation IEEE室内无缝定位与导航学报

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-01-06 DOI: 10.1109/JISPIN.2026.3651879

引用次数: 0

Performance Evaluation of Galileo HAS SIS and IDD Channels During the Initial Service Phase Galileo HAS SIS和IDD信道在初始服务阶段的性能评估

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-01-01 Epub Date: 2026-03-16 DOI: 10.1109/JISPIN.2026.3671679

Paolo Zoccarato;Antoine Grenier;Ciro Gioia;Joaquim Fortuny Guash;Matteo Paonni

{"title":"Performance Evaluation of Galileo HAS SIS and IDD Channels During the Initial Service Phase","authors":"Paolo Zoccarato;Antoine Grenier;Ciro Gioia;Joaquim Fortuny Guash;Matteo Paonni","doi":"10.1109/JISPIN.2026.3671679","DOIUrl":"https://doi.org/10.1109/JISPIN.2026.3671679","url":null,"abstract":"Many industrial and scientific applications rely on high accuracy positioning. With the advancements of modern Global Navigation Satellite Systems constellations and the release of new services, access to a real-time high-accuracy positioning anywhere in the world is a reality. To enable real-time precise point positioning, Galileo High Accuracy Service (HAS) offers corrections without needing any additional infrastructures on the user side. In this article, we compare the user positioning performance obtained with HAS corrections received via Internet Data Dissemination and Signal-In-Space using the software JRC User Navigation Engine, which implement the Galileo HAS reference user algorithm developed at the Joint Research Centre. The comparison is carried out using stations of the International GNSS Service located throughout the world, using data acquired since the start of the initial service phase (January 2023). The goal of the article is to evaluate the achievable performance using the different HAS corrections, by reviewing the performance evolution of HAS since the beginning of the initial service phase, and determining the impact on the user performances.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"150-160"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11434963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796274","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

Nonlinear Chirp Interference Mitigation in Snapshot GNSS Receivers Using Piecewise Least Squares Estimation With Fractional Fourier Transform 基于分数阶傅里叶变换分段最小二乘估计的快照GNSS接收机非线性啁啾干扰抑制

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-01-01 Epub Date: 2026-04-03 DOI: 10.1109/JISPIN.2026.3677784

Xavier Álvarez-Molina;Gonzalo Seco-Granados;José A. López-Salcedo

{"title":"Nonlinear Chirp Interference Mitigation in Snapshot GNSS Receivers Using Piecewise Least Squares Estimation With Fractional Fourier Transform","authors":"Xavier Álvarez-Molina;Gonzalo Seco-Granados;José A. López-Salcedo","doi":"10.1109/JISPIN.2026.3677784","DOIUrl":"https://doi.org/10.1109/JISPIN.2026.3677784","url":null,"abstract":"Global Navigation Satellite Systems (GNSSs) face increasing vulnerability to radio frequency interference, with chirp signals representing a particularly challenging threat due to their time-varying frequency characteristics that disrupt receivers across wide bandwidths. This study introduces a novel interference mitigation technique that partitions the interferer into segments, leveraging the fractional Fourier transform (FrFT) to enhance suppression. The proposed algorithm employs a piecewise least squares estimation method in combination with fractional-domain processing, specifically aimed at manage nonlinear wideband chirp interference impacting snapshot GNSS receivers. A generalized cross validation metric is used to balance algorithmic complexity with fitting accuracy in each segment. Mitigation occurs in the fractional domain, allowing interference energy to be concentrated at fractional coordinates matching the chirp characteristics, thereby reducing the number of affected GNSS samples compare to traditional pulse blanking. The method demonstrates particular robustness against linear frequency modulated (LFM) chirps, and for quadratic frequency modulated signals, it further divides the interference into approximately LFM segments for targeted FrFT-based suppression. Experimental results show that this approach preserves signal quality more effectively than conventional mitigation strategies.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"106-115"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11474667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696179","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

Bayesian Estimation on Lie Group With Von Mises Distribution: Application to R-Mode Phase Denoising Von Mises分布李群的贝叶斯估计及其在r -模相位去噪中的应用

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-01-01 Epub Date: 2026-03-13 DOI: 10.1109/JISPIN.2026.3673669

Esteban Morales Aguirre;Samy Labsir;Benoît Priot;Filippo Giacomo Rizzi;Clément Gazzino;Gaël Pages

{"title":"Bayesian Estimation on Lie Group With Von Mises Distribution: Application to R-Mode Phase Denoising","authors":"Esteban Morales Aguirre;Samy Labsir;Benoît Priot;Filippo Giacomo Rizzi;Clément Gazzino;Gaël Pages","doi":"10.1109/JISPIN.2026.3673669","DOIUrl":"https://doi.org/10.1109/JISPIN.2026.3673669","url":null,"abstract":"Several precise positioning systems rely on carrier phase measurements, which enable centimeter-level accuracy. However, these measurements are significantly affected by additive noise, degrading the performance of phase-based positioning estimators. To better capture the statistical behavior of carrier phase observations, we propose to model phase measurements with a von Mises distribution. Then, to perform precise navigation, it is fundamental to estimate its parameters. To ensure estimation consistency and the respect of the underlying geometric constraints, we propose to perform their estimation within a Lie group (LG) framework. Furthermore, we adopt a Bayesian approach, where prior information about the parameters is assumed within the space <inline-formula><tex-math>$text{SO}(2) times mathbb {R}^+$</tex-math></inline-formula>. The proposed methodology provides a full Bayesian formulation that incorporates prior knowledge, solved through a Newton algorithm on LGs. This approach demonstrates advantages in terms of robustness and precision, especially when dealing with a small number of observations, compared to traditional Euclidean-based and frequentist methods. Furthermore, we validate the approach through simulations with a real navigation dataset.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"116-127"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11433730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796273","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

Simulated GNSS Signal Strength Variation Under Leaky Feeder Segments in a Tunnel—A Case Study 隧道馈线漏段下GNSS信号强度变化模拟研究

IF 2.9

IEEE Journal of Indoor and Seamless Positioning and Navigation Pub Date : 2026-01-01 Epub Date: 2026-09-07 DOI: 10.1109/JISPIN.2026.3731198

Oskari Iivarinen;Sylvain Daubas;Maaria Nordman

{"title":"Simulated GNSS Signal Strength Variation Under Leaky Feeder Segments in a Tunnel—A Case Study","authors":"Oskari Iivarinen;Sylvain Daubas;Maaria Nordman","doi":"10.1109/JISPIN.2026.3731198","DOIUrl":"https://doi.org/10.1109/JISPIN.2026.3731198","url":null,"abstract":"In this article, we studied rapid, centimeter-scale oscillations in received GNSS signal strength under a leaky feeder in a tunnel. GPS L1 signals were generated with a commercial GNSS simulator and injected into both ends of a radiating cable, after which the carrier-to-noise ratio was recorded with a mobile receiver moving incrementally along a rail. We developed a simplified, tunable propagation model that represents 1) signal attenuation and delay inside the leaky feeder, 2) radiation from multiple slot locations, and 3) reception directly below the cable, and ran it in two versions, with and without a term representing a wave reflected from the far end of the cable. Recorded and simulated signals were aligned and compared on three quantities: correlation, oscillation spacing, and oscillation amplitude. At 50 m from the injection point, the version with the reflected wave agreed with the measurement significantly better than the one without it (bandpassed correlation 0.88 against 0.63), in two independent sessions, reproducing the measured 10.3 cm spacing to within 0.1 cm and the amplitude to within about 20% with nothing adjusted. At the other locations the difference between the two versions was not significant, but none favored the version without the reflected wave. The results indicate that a standing wave can account for part of the observed oscillation and that a simple model reproduces the measured spacing and amplitude, supporting future work on GNSS-based positioning and augmentation in tunnels.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"4 ","pages":"192-203"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11680520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034357","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