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TechRxiv 2023年

作者： Do, Hoang Viet Jung, Jae Hyung Park, Chan Gook Song, Jin Woo The Intelligent Navigation and Control Systems Laboratory Department of Convergence Engineering for Intelligent Drone School of Intelligent Mechatronics Engineering Sejong University Seoul05006 Korea Republic of The Department of Aerospace Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of The Navigation and Electronic System Laboratory Department of Aerospace Engineering Institute of Advanced Aerospace Technology Seoul National University Seoul08826 Korea Republic of

In recent years, the error-state Kalman filter (ErKF) has been extensively employed across various applications, including but not limited to robotics, aerospace, and localization. However, incorporating constraints into the ErKF framework when state constraint is necessary has remained a challenging task due to its intrinsic properties. This paper explores all possible ways to achieve this goal in the context of the estimate projection method. In particular, the constraint can be enforced before or after the ErKF’s correction step. We approach the problem from a mathematical perspective by deriving analytical solutions and discussing their statistical properties. We prove that the two mentioned methods are statistically identical for a linear system with linear constraints. Conversely, the filter’s behavior remains uncertain in the presence of linearized constraints. However, we provide a special case of the nonlinear constraint, wherein the results of the linear case remain valid. To support our theorem and verify the filter’s performance when the assumptions are invalidated, we present two Monte Carlo simulations under the increasing initialization error and the constraint’s incompleteness. The simulation results clearly confirm our insights and lead to the conclusion that constraining the error-state after the correction may offer superior outcomes compared to its competitor. © 2023, CC BY.

关键词： Linearization

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Towards ISAC-Empowered Vehicular Networks: Framework, Advances, and Opportunities

arXiv

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arXiv 2023年

作者： Du, Zhen Liu, Fan Li, Yunxin Yuan, Weijie Cui, Yuanhao Zhang, Zenghui Masouros, Christos Ai, Bo School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

Connected and autonomous vehicle (CAV) networks face several challenges, such as low throughput, high latency, and poor localization accuracy. These challenges severely impede the implementation of CAV networks for immersive metaverse applications and driving safety in future 6G wireless networks. To alleviate these issues, integrated sensing and communications (ISAC) is envisioned as a game-changing technology for future CAV networks. This article presents a comprehensive overview on the application of ISAC techniques in vehicle-to-infrastructure (V2I) networks. We cover the general system framework, representative advances, and a detailed case study on using the 5G New Radio (NR) waveform for sensing-assisted communications in V2I networks. Finally, we highlight open problems and opportunities in the field. Copyright © 2023, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Analytical Models for QoT Estimations in Super-C and Super-C + L Bands Optical Transmission systems

Analytical Models for QoT Estimations in Super-C and Super-C...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Yan, Baoluo Sun, Yijun Zhou, Ziwen Liu, Hong Wang, Hongya Huan, Chen Zhao, Zhiyong Feng, Lipeng Zhang, Anxu Feng, Zhenhua Shi, Hu WDM System Department Wireline Product R&D Institute ZTE Corp. Beijing100029 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology 518055 China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Optical and Electronic Information Wuhan National Laboratory for Optoelectronics Optics Valley Laboratory Huazhong University of Science and Technology Wuhan China China Telecom Research Institute Beijing102209 China

We propose a novel physical model-based QoT estimation with achieved OSNR estimation error 1.5 dB in primary paths and OSNR prediction error 1.76 dB in unestablished lightpaths for both super-C bands and super-C+L ban... 详细信息

ISBN: (纸本)9781957171258

关键词： Light transmission

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DPNet: Dynamic Pooling Network for Tiny Object Detection

arXiv

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arXiv 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie School of Computer Science Beijing University of Posts and Telecommunications Beijing China Research Center for Space Computing System Zhejiang Lab Hangzhou China SWJTU-LEEDS Joint School Southwest Jiaotong University Chengdu China Guangdong Zhiyun City construction Technology Co. LTD Zhuhai China Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Hohai University China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed downsampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware downsampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Modeling and control of a half Car Active Suspension system using Sliding Mode controller and Linear Quadratic Regulator controller

Modeling and Control of a half Car Active Suspension System ...

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Renewable Energy & Sustainability (ieCRES), International engineering Conference on

作者： Abdussalam Ali Ahmed Hisham.F. A. Ramadhan Mohamed El Malki Yasser Fathi Nassar Mechanical Engineering Department Bani Waleed University Bani Waleed Libya Control System Engineering College of Electronic Technology Bani Walid Libya Department of Physics Faculty of Sciences Mohammed First University Oujda Morocco Mechanical and Renewable Energy Eng. Dept. Wadi Alshatti University Brack Libya

This paper illustrates the use of two distinct control strategies. Namely, the Sliding Mode control (SMC) and a Linear Quadratic Regulator (LQR) controller to monitor and handle the suspension system vibrations that occurred. We used a half car suspension model with (4DOF). The necessary suspension performance attributes, such as ride comfort, suspension deflection and car handling require a comprehensive control system. Three components make up this control system, which is carried out using the MATLAB application: the controller, the suspension system model, and input signals (road profile and actuator force). A comparison of the half car active suspension system LQR and SMC controllers as well as the uncontrolled suspension system is presented.

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Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods

arXiv

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arXiv 2023年

作者： Li, Shilei Li, Lijing Shi, Dawei Lou, Yunjiang Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Information and Control Engineering China University of Mining and Technology China The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the multi-kernel correntropy-based gradient descent (CGD) and the multi-kernel correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the least square (LS) criterion in algorithm derivation, making them vulnerable to external acceleration and magnetic interference. To address this issue, we first demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function under the maximum likelihood estimation (MLE) framework when the noise follows a specific type of heavy-tailed distribution. Then, we provide some important properties of the MKCL as a cost function. By replacing the LS cost with the MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration

arXiv

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arXiv 2023年

作者： Li, Shilei Chen, Yihan Lou, Yunjiang Shi, Dawei Li, Lijing Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Automation Beijing Institute of Technology China The School of Information and Control Engineering China University of Mining and Technology China

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and state that the kernel bandwidth plays an important role in minimizing the lowest upper error bound. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution but its tail shape is heavy-tailed, and the extent of heavy-tail is controlled by the kernel bandwidth. It becomes a Gaussian distribution when the bandwidth is infinite, which allows one to tackle both Gaussian and non-Gaussian problems without explicitly investigating the noise distributions. To explore the optimal underlying distribution parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the distribution parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and it significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method. Note to Practitioners-The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian assumptions. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both th

关键词： Linear regression

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Hybrid Beamforming-based Beam Tracking using Angular speed in Massive MIMO systems

Hybrid Beamforming-based Beam Tracking using Angular speed i...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Seungseok Sin Jihun Cho Huaping Liu Sangmi Moon Yuna Sim Kyunam Kim Jihyung Kim Intae Hwang Deptartment of ICT Convergences System Engineering Chonnam National University Gwangju Republic of Korea Oregon State University United States of America Department of IT Artificial Intelligence Korea Nazarene University Cheonan Republic of Korea Alps Electric Korea Co. Ltd Gwangju Republic of Korea Spatial Wireless Transmission Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea Department of Electronic Engineering and Department of ICT Convergence System Engineering Chonnam National University Gwangju Republic of Korea

This paper proposes an angular speed 3D-based beam tracking method using hybrid beamforming. We propose a new beam tracking algorithm that enables dynamic pilot insertion based on closed loop feedback control by utilizing the Line of Sight (LoS) path of the channel between T-BS and UAV-UE. Simulations show that the proposed beam tracking method has improved overall performance in terms of beamforming gain and spectral efficiency (SE).

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Clustering-Beamforming Algorithm for Security Performance in Multiple Flying RISs-supported systems

Clustering-Beamforming Algorithm for Security Performance in...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Yuna Sim Jihun Cho Huaping Liu Sangmi Moon Seungseok Sin Kyunam Kim Jihyung Kim Intae Hwang Deptartment of ICT Convergences System Engineering Chonnam National University Gwangju Republic of Korea Oregon State University United States of America Department of IT Artificial Intelligence Korea Nazarene University Cheonan Republic of Korea Alps Electric Korea Co. Ltd. Gwangju Republic of Korea Spatial Wireless Transmission Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea Department of Electronic Engineering and Department of ICT Convergence System Engineering Chonnam National University Gwangju Republic of Korea

This paper introduces an algorithm for improving the security performance in a system by combining unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), which are emerging technologies in the context of 6G networks. Multiple flying RISs are utilized in this approach. Among them, a DNN-based algorithm is presented to deploy flying RISs in appropriate locations to cover the maximum number of users possible. Through simulation experiments, the superiority of the proposed algorithm is confirmed in terms of accuracy and execution time. In the future, research will be conducted on algorithms optimizing beamforming from the perspective of the base station (BS) and achieving active beamforming within the RIS to further enhance the security performance.

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

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arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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