IEEE Transactions on Network Science and Engineering最新文献_第6页

Distributed Network Control of Multi-UAV Systems for Cooperative Heavy-Load Transport Using a Virtual-Passivity Framework 基于虚拟无源框架的多无人机协同重载运输分布式网络控制

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-14 DOI: 10.1109/TNSE.2026.3654107

Runxiao Liu;Xiangli Le;Shuang Gu;Shuli Lv;Pengda Mao;Quan Quan

{"title":"Distributed Network Control of Multi-UAV Systems for Cooperative Heavy-Load Transport Using a Virtual-Passivity Framework","authors":"Runxiao Liu;Xiangli Le;Shuang Gu;Shuli Lv;Pengda Mao;Quan Quan","doi":"10.1109/TNSE.2026.3654107","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3654107","url":null,"abstract":"This paper presents a novel distributed network control framework for cooperative heavy-load transportation using multi-UAV systems, accounting for thrust limitations and heterogeneous cable characteristics. By constructing a virtual passive system comprising interconnected virtual nodes, springs, and dampers, the proposed method decouples internal coordination stability from external velocity tracking. A velocity tracking controller is devised to asymptotically steer the load’s velocity toward a desired trajectory, while preserving inter-agent cohesion through virtual interactions. Notably, the controller operates without explicit inter-UAV communication, relying solely on relative position measurements. Numerical simulations involving ten UAVs transporting a 14 kg load-exceeding 76% of their combined thrust capacity-along a figure-eight trajectory validate the proposed method. Field tests with six UAVs transporting a 6 kg load are conducted to validate the control framework’s performance in practical applications. The results confirm accurate velocity tracking, balanced cable tension distribution, and scalability to heterogeneous UAV team configurations.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5905-5923"},"PeriodicalIF":7.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Angle Map-Based Beam Alignment for RIS-Aided mmWave Communication Networks 基于智能角度图的ris辅助毫米波通信网络波束对准

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-13 DOI: 10.1109/TNSE.2026.3653564

Hao Xia;Qing Xue;Yanping Liu;Binggui Zhou;Meng Hua;Qianbin Chen

{"title":"Intelligent Angle Map-Based Beam Alignment for RIS-Aided mmWave Communication Networks","authors":"Hao Xia;Qing Xue;Yanping Liu;Binggui Zhou;Meng Hua;Qianbin Chen","doi":"10.1109/TNSE.2026.3653564","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3653564","url":null,"abstract":"Recently, reconfigurable intelligent surface (RIS) has been widely used to enhance the performance of millimeter wave (mmWave) communication systems, making beam alignment more challenging. To ensure efficient communication, this paper proposes a novel intelligent angle map-based beam alignment scheme for both general user equipments (UEs) and RIS-aided UEs simultaneously in a fast and effective way. Specifically, we construct a beam alignment architecture that utilizes only angular information. To obtain the angle information, the currently hottest seq2seq model – the Transformer – is introduced to offline learn the relationship between UE geographic location and the corresponding optimal beam direction. Based on the powerful machine learning model, the location-angle mapping function, i.e., the angle map, can be built. As long as the location information of UEs is available, the angle map can make the acquisition of beam alignment angles effortless. In the simulation, we utilize a ray-tracing-based dataset to verify the performance of the proposed scheme. It is demonstrated that the proposed scheme can achieve high-precision beam alignment and remarkable system performance without any beam scanning.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5833-5850"},"PeriodicalIF":7.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Socially Inspired Adaptive Framework for Distributed Online Inference 分布式在线推理的社会启发自适应框架

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-13 DOI: 10.1109/TNSE.2026.3653651

Dongyan Sui;Yufei Liu;Siyang Leng

{"title":"Socially Inspired Adaptive Framework for Distributed Online Inference","authors":"Dongyan Sui;Yufei Liu;Siyang Leng","doi":"10.1109/TNSE.2026.3653651","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3653651","url":null,"abstract":"Collective decision-making in networked systems is often shaped not only by peer interactions but also by persistent external influences. This paper introduces an intervened non-Bayesian social learning model thatexplicitly incorporates external information sources—whose beliefs remain fixed and potentially biased—into the belief-update process of a distributed multi-agent network. Analytical characterization of theproposed model reveals that such interventions disrupt strong consensus on the underlying true state, resulting in steady-state belief distributions that exhibit persistent oscillation and even polarization, consistent with empirical social observations. Building upon these insights, we propose a socially inspired adaptive algorithm for distributed online inference, which mitigates the rigidity of traditional non-Bayesian social learning updates and enables agents to remain responsive to environmental changes. Theoretical analysis and numerical experiments demonstrate that the proposed framework achieves enhanced adaptability and accurate online inference while preserving the decentralized cooperation mechanism of non-Bayesian social learning.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5924-5942"},"PeriodicalIF":7.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QoEUP: A Preference-Based QoE Optimization Scheme Using Human Feedback for Mobile Video Streaming QoEUP：基于偏好的移动视频流人机反馈QoE优化方案

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-12 DOI: 10.1109/TNSE.2026.3651560

Hang Yin;Heli Zhang;Jianchi Zhu;Shan Yang;Jianxiu Wang;Peng Chen;Nan Ma

{"title":"QoEUP: A Preference-Based QoE Optimization Scheme Using Human Feedback for Mobile Video Streaming","authors":"Hang Yin;Heli Zhang;Jianchi Zhu;Shan Yang;Jianxiu Wang;Peng Chen;Nan Ma","doi":"10.1109/TNSE.2026.3651560","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3651560","url":null,"abstract":"With the rising popularity of mobile video streaming, dynamic adaptive streaming over HTTP (DASH)-based bitrate adaptation has garnered significant attention in recent years. Existing studies in this area typically define a unified user quality of experience (QoE) by weighting various metrics, overlooking individual user preferences for QoE. Although some research has considered user preferences, it primarily focuses on optimizing bitrate selection alone, neglecting the joint allocation of communication resources that are tightly coupled with bitrate. In this paper, we propose QoE Optimization Enabler based on User Preference (QoEUP), a scheme for mobile video streaming, which dynamically adjusts bitrate, transmission power, and bandwidth based on channel quality and user preferences during mobility. The proposed scheme begins with training a reference model using deep reinforcement learning without incorporating user preferences. We then develop a user-friendly approach to collect user preferences and create a preference dataset. Finally, leveraging this dataset, we apply advanced direct preference optimization (DPO) to fine-tune the baseline model through supervised learning, effectively integrating individual QoE preferences. Simulation results demonstrate that QoEUP effectively aligns users' actual viewing experiences with their preferences in terms of video quality, playback smoothness, and device energy consumption.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6156-6173"},"PeriodicalIF":7.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Communication-Efficient Federated Reinforcement Learning for Edge Offloading in AGI-MEC Systems 面向AGI-MEC系统边缘卸载的高效通信联邦强化学习

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-12 DOI: 10.1109/TNSE.2026.3651664

Chenchen Fan;Qingling Wang;Shulong Zhao

{"title":"Communication-Efficient Federated Reinforcement Learning for Edge Offloading in AGI-MEC Systems","authors":"Chenchen Fan;Qingling Wang;Shulong Zhao","doi":"10.1109/TNSE.2026.3651664","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3651664","url":null,"abstract":"Air–ground integrated mobile edge computing (AGI-MEC) integrates aerial and terrestrial resources to provide efficient computing services for massive mobile terminals (MTs), enabling 6G network intelligence. However, jointly optimizing offloading and power control remains challenging due to dynamic channels, resource constraints, communication overhead, and privacy concerns. To address these issues, this paper proposes a clustering-based two-layer federated deep reinforcement learning (CTL-FDRL) algorithm. A clustered federated training framework with parameter sharing is first developed, in which raw data exchange is replaced by periodic model aggregation, enabling MTs to learn offloading policies locally while preserving privacy. Furthermore, an efficient representative MT selection method is introduced. A self-organizing map (SOM)-based MT clustering method is designed to adaptively group MTs without a predefined number of clusters. Guided by the derived convergence bound, representative selection within each cluster is posed as a linear programming problem. The resulting representatives are used for federated aggregation, which reduces communication overhead without degrading model performance. Simulation results verify the superiority of CTL-FDRL, achieving about 28.7% higher cumulative reward, 31.5% lower delay, and 18.3% lower energy consumption compared with baseline algorithms.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5622-5637"},"PeriodicalIF":7.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedDSPT: A Cost-Efficient and Low-Latency Federated Learning Framework Over Non-IID Data FedDSPT：一个基于非iid数据的低成本、低延迟的联邦学习框架

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-12 DOI: 10.1109/TNSE.2026.3652982

Xumin Huang;Zican Huang;Weifeng Zhong;Maoqiang Wu;Ming Li;Shengli Xie

{"title":"FedDSPT: A Cost-Efficient and Low-Latency Federated Learning Framework Over Non-IID Data","authors":"Xumin Huang;Zican Huang;Weifeng Zhong;Maoqiang Wu;Ming Li;Shengli Xie","doi":"10.1109/TNSE.2026.3652982","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3652982","url":null,"abstract":"Federated Learning (FL) enables collaborative model training across decentralized edge devices while preserving data privacy. However, conventional FL frameworks face challenges due to data heterogeneity (non-IID), which impedes convergence, increases energy consumption and latency, and exposes the system to the data poisoning attack. To address the limitations, we propose a novel FL framework termed Federated Learning with Distributed Serial Pipeline Training (FedDSPT). On the client side, FedDSPT employs a dual grouping mechanism that organizes the edge devices into collaborative pipelines based on the feature similarity and diversity. The mechanism promotes more homogeneous intra-pipeline data distributions that approximate IID conditions, improving convergence behavior and reducing resource overhead. To optimize the pipeline formation, we apply the Held-Karp algorithm to determine minimal-cost, non-cyclic communication paths among the device groups. During the SPT phase, we incorporate the adversarial training through controlled injection of noise, enhancing robustness against the data poisoning attack arising from heterogeneous or malicious data sources. On the server side, FedDSPT utilizes an asynchronous pipeline-terminal model updates combined with the buffered aggregation technique to ensure timely and efficient global model synchronization. Experiments show that FedDSPT reduces energy consumption by 31.2% and training time by 26.7%, while demonstrating strong robustness and scalability under the large-scale deployments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5870-5887"},"PeriodicalIF":7.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain Intelligence Empowered Task Offloading and Resource Trading for Secure Low-Altitude Economy Networking 安全低空经济网络的智能任务卸载和资源交易

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-12 DOI: 10.1109/TNSE.2026.3653417

Yihang Huang;Yishuo Chen;Li Zhou;Jiaxin Ren;Hanxue Li;Xiaojie Wang;Zhaolong Ning

{"title":"Blockchain Intelligence Empowered Task Offloading and Resource Trading for Secure Low-Altitude Economy Networking","authors":"Yihang Huang;Yishuo Chen;Li Zhou;Jiaxin Ren;Hanxue Li;Xiaojie Wang;Zhaolong Ning","doi":"10.1109/TNSE.2026.3653417","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3653417","url":null,"abstract":"With the rapid development of Low-Altitude Economy Network (LAEN), how to achieve efficient, secure, and privacy-preserving task completion in complex air-ground collaborative environments has become a critical issue. This paper proposes a joint optimization framework based on blockchain intelligence, which aims to solve the challenges of task offloading, resource trading, and data security in LAEN by deeply integrating the security and trust mechanisms of blockchain with the dynamic decision-making capabilities of Deep Reinforcement Learning (DRL). First, a system model integrating secure task offloading and efficient resource trading is constructed, enabling collaborative work between air and ground nodes through cross-chain technology. To ensure data confidentiality and integrity, Byzantine Fault Tolerance (BFT) theory is used to quantitatively evaluate the system's security. Next, this paper utilizes a series of DRL approaches to jointly optimize relay chain node selection, communication decisions, task offloading decisions, and computational resource allocation. To further enhance the system's privacy protection capabilities, we design an improved Payment Channel Network (PCN) routing scheme to maximize transaction security and privacy. Finally, extensive simulation results show that the proposed scheme has significant advantages over existing algorithms in terms of task execution latency, average utility and transaction success rate.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6470-6487"},"PeriodicalIF":7.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Core Algorithms for Automated Mine Ventilation Network Diagram Generation 矿井通风网络图自动生成核心算法的实现

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-12 DOI: 10.1109/TNSE.2026.3651634

Jinyang Dong;Junqiao Li;Yucheng Li;Bolun Li;Yunan Cui;Zhitao Zhang;Wei Zhang;Jinzhe Liu

{"title":"Implementation of Core Algorithms for Automated Mine Ventilation Network Diagram Generation","authors":"Jinyang Dong;Junqiao Li;Yucheng Li;Bolun Li;Yunan Cui;Zhitao Zhang;Wei Zhang;Jinzhe Liu","doi":"10.1109/TNSE.2026.3651634","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3651634","url":null,"abstract":"Ventilation network diagrams play a vital role in mine safety, enabling simulation, airflow control, and emergency planning. Traditional manual drawing methods are time-consuming, error-prone, and difficult to synchronize with evolving mine structures. To address this, we propose an automated framework that generates schematic ventilation diagrams from spatial models while preserving topological fidelity. The framework integrates a hierarchical node layering algorithm based on Hasse diagrams, a trunk–branch decomposition strategy for structural abstraction, and a deformation-based curve layout model inspired by water droplet geometry to enhance visual clarity and structural alignment. A 3D elevation inference module ensures semantic consistency across 2D diagrams, 3D models, and graph representations. The system further supports reactive multi-view synchronized editing, enabling coherent modifications across different representations while preserving user-defined constraints. Although developed and validated in mine ventilation, the framework is generally applicable to directed-graph–based industrial systems. Its generality is further demonstrated on a schematic exhaust pipeline case, where a tree structure rather than a network diagram provides a more suitable abstraction, highlighting that the method supports domain-adaptive abstraction guided by topological characteristics. This approach has been integrated into real-world mining operations through the “Ventilation Brain System,” offering a scalable solution for intelligent layout generation, adaptive design iteration, and responsive decision-making. By bridging spatial realism with schematic abstraction, the proposed method streamlines diagram construction and reinforces intelligent mine ventilation management.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6079-6105"},"PeriodicalIF":7.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Disconnection-Resistant Authenticated Diffie-Hellman Protocol in Wireless Weak-Link Sensor Networks 无线弱链路传感器网络中抗断开认证的Diffie-Hellman协议

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-12 DOI: 10.1109/TNSE.2026.3653104

Jia Zhang;Guanghua Liu;Chenlong Wang;Zhihao Liu;Tao Jiang

{"title":"A Disconnection-Resistant Authenticated Diffie-Hellman Protocol in Wireless Weak-Link Sensor Networks","authors":"Jia Zhang;Guanghua Liu;Chenlong Wang;Zhihao Liu;Tao Jiang","doi":"10.1109/TNSE.2026.3653104","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3653104","url":null,"abstract":"The application of wireless sensor networks (WSNs) in underground or underwater regions is becoming increasingly prevalent. This specific type of WSNs are referred to as wireless weak-link sensor networks(WWSNs), whose key distribution is identified as a prerequisite for ensuring network security. However, due to the fragile links of WWSNs, it is not feasible to directly transfer existing authenticated Diffie-Hellman (ADH) protocols from traditional WSNs to WWSNs. To address this challenge, we propose a disconnection-resistant authenticated Diffie-Hellman protocol (D-ADH) for key distribution in WWSNs. For mitigating the adverse impact of fragile links, we significantly reduce the number of node interactions in existing ADH protocols by having the sensor node use fixed negotiation public keys. Each sensor node only needs to receive the broadcast message of the base station on a single occasion to generate the session key. The results of simulation experiments and security analysis demonstrate that the proposed D-ADH protocol exhibits the lowest energy consumption, the longest network lifetime, the highest probability of successful key distribution and good network scalability compared to the state-of-the-art protocols, while maintaining an acceptable middle level security.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6010-6026"},"PeriodicalIF":7.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Soft Information Fusion for Tracking Multiple Targets in NLoS Environments NLoS环境下多目标跟踪的软信息融合

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-05 DOI: 10.1109/TNSE.2026.3650814

Yuhan Wang;Yinuo Du;Jian Wang;Yuan Shen

{"title":"Soft Information Fusion for Tracking Multiple Targets in NLoS Environments","authors":"Yuhan Wang;Yinuo Du;Jian Wang;Yuan Shen","doi":"10.1109/TNSE.2026.3650814","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3650814","url":null,"abstract":"An efficient approximation of the latent soft information (SI) is a key enabler for tracking multiple targets in time-varying non-line-of-sight (NLoS) environments. Such information can be approximated by associating numerous NLoS observations with the targets’ movement, a novel paradigm that transitions from the traditional “tracking after localization” approaches to a more comprehensive “tracking with localization” method. In particular, we establish the extended states (ETs) for targets to associate with their possible measurements in multipath scenarios. The joint distribution of ETs is regarded as an approximation of the latent SI at the sensor level. The information carried by ETs is then transferred to the target level for tracking using a variational message passing approach, and subsequently fed back to the sensor level to estimate the SI for future steps. We establish a factor graph depicting the propagation of information from multi-sensor measurements to multi-target states and vice versa. Numerical results including uncrewed aerial vehicles (UAVs) validate the superior performance of our proposed algorithm compared to alternative approaches.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5663-5679"},"PeriodicalIF":7.9,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0