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Non-cooperative target recognition and relative motion estimation with inertial measurement unit assistance

Chinese Journal of Aeronautics 2025年第4期38卷

作者： ZHAO, Xiangtian WANG, Shiqiang ZHANG, Chao ZHANG, Shijie ZHAO, Yafei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Beijing Institute of Remote Sensing Equipment Beijing100854 China Research Center of Satellite Technology Harbin Institute of Technology Harbin150080 China Communication Technology Co. Ltd. Beijing100192 China

This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers. A structure integrating an Inertial Measurement Unit (IMU) and a visual camera was presented. The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames, which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition. To solve the problem of target misidentification caused by new star points entering the field of view, a target-tracking link based on IMU prediction was introduced to track the position of the target in the image. Furthermore, a measurement model was constructed using the line-of-sight vector generated from target recognition, and the relative motion state was estimated using a Huber-based non-linear filter. Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method. © 2024

关键词： Target tracking

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TSVC: Tripartite Learning with Semantic Variation Consistency for Robust Image-Text Retrieval

arXiv

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arXiv 2025年

作者： Lyu, Shuai Tian, Zijing Ou, Zhonghong Zhu, Yifan Zhang, Xiao Ha, Qiankun Luo, Haoran Song, Meina School of Computer Science Beijing University of Posts and Telecommunications China School of Science Beijing University of Posts and Telecommunications China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

Cross-modal retrieval maps data under different modality via semantic relevance. Existing approaches implicitly assume that data pairs are well-aligned and ignore the widely existing annotation noise, i.e., noisy correspondence (NC). Consequently, it inevitably causes performance degradation. Despite attempts that employ the co-teaching paradigm with identical architectures to provide distinct data perspectives, the differences between these architectures are primarily stemmed from random initialization. Thus, the model becomes increasingly homogeneous along with the training process. Consequently, the additional information brought by this paradigm is severely limited. In order to resolve this problem, we introduce a Tripartite learning with Semantic Variation Consistency (TSVC) for robust image-text retrieval. We design a tripartite cooperative learning mechanism comprising a Coordinator, a Master, and an Assistant model. The Coordinator distributes data, and the Assistant model supports the Master model’s noisy label prediction with diverse data. Moreover, we introduce a soft label estimation method based on mutual information variation, which quantifies the noise in new samples and assigns corresponding soft labels. We also present a new loss function to enhance robustness and optimize training effectiveness. Extensive experiments on three widely used datasets demonstrate that, even at increasing noise ratios, TSVC exhibits significant advantages in retrieval accuracy and maintains stable training performance. © 2025, CC BY.

关键词： Semantics

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Joint Video Frame Scheduling and Resource Allocation for Device-Edge Collaborative Video Intelligent Analytics

Joint Video Frame Scheduling and Resource Allocation for Dev...

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IEEE Conference on Wireless Communications and networking

作者： Jiayi Li Xiaoyu Chi Hui Wang Yi Su Shujun Han Xiaodong Xu Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology National Engineering Research Center of Mobile Network Technologies §Migu Interactive Entertainment Co. Ltd.

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the development of 6G immersive communication, video intelligent analytics has garnered significant attention. Video intelligent analytics has diverse requirements in different immersive service scenarios, especially in accuracy and latency. However, as resource-limited terminal devices struggle to accom-plish high-accuracy video intelligent analytics tasks, video frames have to be offloaded to edge nodes with sufficient computational and cache resources for further processing. Therefore, in this paper, we consider device-edge collaboration video intelligent an-alytics tasks to improve trade-off performance between accuracy and latency. Specifically, we propose a joint optimization scheme for video frame scheduling, adaptive video frame compression and Machine Learning (ML) model caching to maximize the minimum of utility among all users. We divide the joint optimization problem into two sub-problems and use convex optimization to solve the adaptive frame compression optimization problem. Furthermore, to avoid the curse of dimensionality, we design an expert-assisted proximal policy optimization (EPPO)-based joint video frame scheduling and resource allocation algorithm. Simulation results demonstrate the superiority of the proposed scheme in improving video intelligent analytics performance.

关键词： Performance evaluation Adaptation models Analytical models Adaptive scheduling Accuracy Simulation Collaboration Convex functions Resource management Optimization

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CaRCS: Joint Optimization of Computing-aware Routing and Collaborative Scheduling in Computing Power Networks

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IEEE Network 2025年

作者： Feng, Li Xie, Renchao Tang, Qinqin Huang, Tao Xiong, Zehui Chen, Tianjiao Zhang, Ran Tan, Sha Fang, Zeru Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Purple Mountain Laboratories Nanjing211111 China Singapore University of Technology and Design Singapore Pillar of Information Systems Technology and Design 639798 Singapore China Mobile Research Institute Beijing100053 China

With the rapid advancement of technology, intelligent applications increasingly require higher computing power, driving the evolution of the end-edge-cloud Collaborative Scheduling (CS) paradigm. However, this paradigm primarily focuses on vertical collaboration, neglecting horizontal coordination among ubiquitous heterogeneous computing resources, leading to imbalanced resource utilization. Fortunately, the Computing Power Network (CPN) has been introduced to facilitate ubiquitous resource coordination through pervasive networks. The core issue in CPN is the CS of network-wide computing tasks, yet most existing research has largely concentrated on offloading decisions while critical aspects of network routing remain underexplored. Therefore, this paper studies the optimization of joint routing and scheduling within large-scale CPNs. Initially, we develop a CS framework that accommodates the return of task computing results. We then incorporate computing information into the routing domain and introduce a CS mechanism based on Compute-aware Routing (CaR), which narrows the scope of routing exchanges and collaborative scheduling. Ultimately, we present a Deep Reinforcement Learning (DRL)-based algorithm to optimize the long-term CS problems based on balanced CaR, enhancing the success rate of computing tasks and ensuring balanced utilization of computing and network resources. The effectiveness of our proposed mechanism and algorithm is confirmed through simulation experiments. © 2025 IEEE.

关键词： Scheduling algorithms

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CrossTrace: Privacy-aware Cross-System Trajectory Recovery via Hybrid Split and Federated Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Cao, Zijian Zhao, Dong Wang, Qiyue Yuan, Haitao Ma, Huadong Yu, Shui Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Computer Science Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Technology Sydney School of Computer Science UltimoNSW2007 Australia

Massive urban-scale vehicle trajectories benefit various downstream applications. However, trajectories collected from existing sensing systems are often incomplete, necessitating the recovery of coarse-grained trajectories. Considering that mobility knowledge learned from a single system is less representative of all vehicles or covers only partial road segments, it becomes essential to combine diverse data from multiple systems to support trajectory recovery. Therefore, we learn the impacts of mobility intentions and dynamic traffic conditions on the movement of vehicles from trajectories aggregated across different systems to recover their travel routes on unobservable road intersections. Nonetheless, aggregating raw data across multiple systems raises privacy concerns. This data isolation compounds challenges in acquiring comprehensive mobility intentions and traffic conditions, thereby impairing recovery performance. In this paper, we propose CrossTrace, a two-stage framework for privacy-aware cross-system trajectory recovery: in the Traffic Condition Inference stage, a Split Learning pipeline with a multi-view graph neural network is utilized to infer complete traffic conditions for all road segments;in the Trajectory Recovery stage, a Federated Learning pipeline with dedicated modules is utilized to recover missing points by fusing inferred traffic conditions and mobility intentions. Extensive experiments on two large-scale trajectory datasets demonstrate that CrossTrace outperforms all alternative schemes. © 2002-2012 IEEE.

关键词： Graph neural networks

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Semantic Prior Aided Channel-Adaptive Equalizing and De-Noising Semantic Communication System with Latent Diffusion Model

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Bingxuan Han, Shujun Xu, Xiaodong Li, Weizhi Meng, Rui Dong, Chen Zhan, Ping Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Shenzhen518066 China Beijing University of Posts and Telecommunications National Engineering Research Center for Mobile Network Technologies Beijing100876 China

Semantic Communication (SemCom) has opened a new paradigm in the 6G system. However, the performance of SemCom can be severely affected by time-varying path loss, channel noises, and other interference in wireless channels. Therefore, we propose a novel Semantic Prior aided Channel-adaptive Equalizing and De-noising SemCom (SP-EDNSC) framework, where adaptive elimination channel impact is regarded as an inverse problem. This inverse problem is addressed through semantic priors learned from score-based generative models cached in knowledge base. To reduce distortion while enhancing perceptual quality, we further combine autoencoders, adversarial learning and diffusion models to develop a latent diffusion-based (SP-Latent-Diff EDNSC) system within the SP-EDNSC framework. In the semantic space, the joint semantic equalizer and de-noiser module utilizes the proposed latent diffusion posterior sampling method. This method iteratively executes a modified reverse stochastic differential equation to sample clean semantic features, using the time-dependent score function of likelihood and semantic priors. The semantic priors are derived from pre-trained latent diffusion models, while the likelihood is approximated by a multivariate normal distribution. Simulations demonstrate that our scheme achieves superior performance in both distortion metrics like PSNR and SSIM, as well as in perceptual performance (LPIPS). © 2025 IEEE. All rights reserved.

关键词： Inverse problems

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Integrated Communication and Navigation Enabled Low Earth Orbit Satellite Systems

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IEEE Network 2025年

作者： Liu, Binghong Peng, Mugen Li, Jiajing Yang, XuXu Liu, Yuanwei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

Relying on the powerful communication capabilities, rapidly changing geometric configuration, and strong power, Low Earth Orbit (LEO) satellites own the potential to offer the integrated communication and navigation (ICAN) services with broader coverage, higher precision, and stronger reliability. Against this backdrop, LEO-ICAN has been attracting wide research attention. However, despite the above promising potential, many challenges are still unresolved in terms of architecture, key technologies and unified performance characterization. Therefore, in this article, we first elaborate three evolutionary stages of LEO-ICAN, where a fusional LEO-ICAN architecture is innovatively proposed to better meet the high-speed communication and high-precision navigation requirements of the emerging applications. Moreover, key technologies are discussed, and a use case referring to the joint optimization in fusional LEO-ICAN systems is studied, where we jointly optimize the satellite selection and beamforming design to simultaneously balancing the communication and navigation performance. Effectiveness of our proposed scheme is proved through extensive simulations. In addition, a performance evaluation index for LEO-ICAN systems is established based on the minimum variance unbiased estimate (MVUE), providing theoretical guidance and support for the improvement of both communication and navigation performance. Finally, open issues and future research directions are discussed. © 2024 IEEE.

关键词： Satellite communication systems

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Intelligence Sharing in LEO Satellite Edge Computing Networks: A Coalition-based Approach

Intelligence Sharing in LEO Satellite Edge Computing Network...

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IEEE Conference on Wireless Communications and networking

作者： Zeru Fang Qinqin Tang Renchao Xie Tao Huang Tianjiao Chen Ran Zhang Sha Tan Li Feng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing P.R. China Purple Mountain Laboratories Nanjing P.R. China China Mobile Research Institute

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we propose an innovative architecture for sharing intelligence in low earth orbit (LEO) satellite edge computing networks. Specifically, we adopt the sharing of intel-ligence to satellites via ground stations to improve the response speed of satellites in processing intelligent services. Considering the burden of frequent transmission of intelligent models over unstable ground-satellite links, the satellites share intelligence with each other in the coalition, which greatly reduces the service response delay. In addition, considering the poor generalization of pre-trained intelligent models transmitted by ground stations, we design a model aggregation scheme with differentiated weights. Each coalition appoints a coalition center satellite, tasked with aggregating models and re-sharing them to individual satellites, thereby enhancing model performance. Then, we propose two low-time complexity algorithms to solve the above problems. Finally, the effectiveness and superiority of the proposed schemes are verified through extensive simulations.

关键词： Satellites Computational modeling Low earth orbit satellites Computer architecture Delays Complexity theory Edge computing

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Intelligent Joint Security and Delay Determinacy Performance Guarantee Strategy in RIS-Assisted IIoT Communication Systems

arXiv

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arXiv 2025年

作者： Meng, Rui Meng, Zhuo Lu, Jiaqi Xu, Xiaodong Zhang, Jingxuan Cheng, Xiqi Dong, Chen State Key Laboratory of Networking and Switching Technology BUPT Beijing China College of Electronics and Information Engineering Beibu Gulf University Qinzhou China Department of Broadband Communication Peng Cheng Laboratory Shenzhen China National School of Elite Engineering University of Science and Technology Beijing Beijing China

With the advancement of the Industrial Internet of Things (IIoT), IIoT services now exhibit diverse Quality of Service (QoS) requirements in terms of delay, determinacy, and security, which pose significant challenges for alignment with existing network resources. Reconfigurable Intelligent Surface (RIS), a key enabling technology for IIoT, not only optimizes signal propagation and enhances network performance but also ensures secure communication and deterministic delays to mitigate threats such as data leakage and eavesdropping. In this paper, we conduct a deterministic delay analysis under a specified decoding error rate for RIS-assisted IIoT communication systems using Stochastic Network Calculus (SNC). We propose an on-demand joint strategy to maximize delay determinacy while guaranteeing secure transmission performance. This is achieved by jointly optimizing the transmit power, channel blocklength (CBL) at the user end, and the phase shift matrix at the RIS. Furthermore, we introduce a state Interdependence-Driven Parameterized Deep Q-Network (SID-PDQN) algorithm to intelligently enforce on-demand performance guarantees. Simulation results demonstrate that the proposed SID-PDQN algorithm significantly enhances network performance compared to baseline methods such as DQN, Dueling-DQN, and DDPG. Copyright © 2025, The Authors. All rights reserved.

关键词： Transmission network calculations

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Priority-Aware Task Scheduling in Computing Power Network-enabled Edge Computing Systems

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IEEE Transactions on Network Science and Engineering 2025年

作者： Xie, Renchao Feng, Li Tang, Qinqin Zhu, Han Huang, Tao Zhang, Ran Yu, F. Richard Xiong, Zehui Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Purple Mountain Laboratories Nanjing211111 China Carleton University School of Information Technology OttawaONK1S 5B6 Canada Singapore University of Technology and Design 639798 Singapore

The Internet of everything, a potential direction for the next-generation Internet, positions edge collaboration as a promising computing paradigm to address the workload dispersion and resource constraints inherent in traditional edge computing frameworks. However, the increasing complexity of cross-domain networks introduces challenges for efficient task execution and balanced resource utilization in edge collaboration, which remain insufficiently explored. To address these challenges, a next-generation network architecture, the compute power network (CPN), was recently proposed. The CPN leverages ubiquitous connections among heterogeneous resources to optimize task scheduling collaboratively. Building on this concept, we design an edge computing system that integrates CPN to enable dynamic and collaborative task scheduling. Inspired by the sliding window, we develop a dynamic scheduling scheme that prioritizes computing tasks and matches tasks to computing resources in real time. Additionally, we propose an improved deep reinforcement learning (DRL) algorithm to optimize scheduling policies, aiming to improve task success rates, minimize execution delays, and ensure balanced and efficient resource utilization. Lastly, simulation experiments validate the effectiveness of the proposed scheme and algorithm. © 2013 IEEE.

关键词： Computing power

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