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Global-local feature optimization based RGB-IR fusion object detection on drone view 认领 引用 被引量：1
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作者 Zhaodong CHEN Hongbing JI Yongquan ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期436-453,共18页
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st... Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at http://gffzz188fe103f8f1460asfccnxf5ob05c6bw0.ffgz.tsg.suse.edu.cn/lao chen330/GLFDet. 展开更多
关键词 Object detection Deep learning RGB-IR fusion Drones Global feature Local feature
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Fault Diagnosis of Wind Turbine Blades Based on Multi-Sensor Weighted Alignment Fusion in Noisy Environments 认领 引用
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作者 Lifu He Zhongchu Huang +4 位作者 Haidong Shao Zhangbo Hu Yuting Wang Jie Mei Xiaofei Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第3期1401-1422,共22页
Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operati... Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis. 展开更多
关键词 Wind turbine blade multi-sensor fusion fault diagnosis CNN-transformer coupled architecture
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Active vibration isolation based on absolute-relative dynamic stiffness control via multi-sensor information fusion 认领 引用
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作者 Zhiwei Huang Jiulin Wu +4 位作者 Fuxiang Zhang Rui Zhou Hu Li Xuedong Chen Wei Jiang 《ENGINEERING Mechanical Engineering》 SCIE CAS CSCD 2026年第2期129-152,共24页
The requirements for isolating outer vibration and suppressing inner disturbances are increasingly stringent and even approaching extreme limits in integrated circuit manufacturing,precision measurement,scientific exp... The requirements for isolating outer vibration and suppressing inner disturbances are increasingly stringent and even approaching extreme limits in integrated circuit manufacturing,precision measurement,scientific experiments,etc.In comparison with passive isolation,active control methods can significantly enhance vibration isolation performance.However,different control strategies are mainly effective in different frequency domains,and performance may deteriorate in some frequency domains due to sensor noises.Active vibration isolation based on absolute-relative dynamic stiffness control via multi-sensor information fusion is proposed in this paper.This method can substantially improve vibration attenuation capability and position stability performances in broad bandwidth,with a particular focus on improving the resonance peak suppression capability in the ultra-low frequency domain.First,the effects of different control strategies on vibration isolation in different frequency domains are analyzed,and the hybrid control strategy is proposed by using both absolute relative signal feedback.Considering the noise characteristics of absolute velocity sensors and relative displacement sensors,different filters are accordingly adopted to improve vibration isolation performance.A one-dimensional experimental platform is established to conduct vibration control experiments under different configurations.The results demonstrate that vibration isolation performance across a wide frequency range can be significantly improved,and the proposed method further proves effective for micro-vibration systems.Typically,transmissibility can be reduced to as low as -30 dB at 1 Hz and -48 dB at 2 Hz,with guarantee of less than -50 dB within 10-50 Hz.Additionally,compliance results show 10-40 dB performance improvements across the broad frequency range(0.1-100 Hz)compared with the passive system. 展开更多
关键词 active vibration control dynamic stiffness multi-sensor information fusion absolute velocity feedback relative displacement feedback
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Multi-Sensor Data Fusion Technologies for Blanket Jamming Localization 认领 引用 被引量：1
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作者 WANG Ju WU Si-liang ZENG Tao 《Journal of Beijing Institute of Technology》 EI CAS 2005年第1期22-26,共5页
The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed.Radars only can acquire angle information without range information when encountering the blanket j... The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed.Radars only can acquire angle information without range information when encountering the blanket jamming.Netted radars could get position information of the blanket jamming by make use of radars'relative position and the angle information,when there is one blanket jamming.In the presence of error,the localization method and the accuracy analysis of one blanket jamming are given.However,if there are more than one blanket jamming,and the two blanket jamming and two radars are coplanar,the localization of jamming could be error due to localization ambiguity.To solve this confusion,the Kalman filter model is established for all intersections,and through the initiation and association algorithm of multi-target,the false intersection can be eliminated.Simulations show that the presented method is valid. 展开更多
关键词 data fusion blanket jamming localization Kalman filter
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Earth Observation for Environmental Security:Emerging Multi-Sensor Fusion Techniques 认领 引用
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作者 Changjiang Cai Lei Gao +2 位作者 Minkuo Cai Fachun She Ruijie Wang 《Journal of Environmental & Earth Sciences》 CAS 2026年第3期91-111,共21页
Climate change,natural disasters,pollution,and fast urbanization have made environmental security a more serious international issue.Timely,accurate,and multi-dimensional information is essential in the effective moni... Climate change,natural disasters,pollution,and fast urbanization have made environmental security a more serious international issue.Timely,accurate,and multi-dimensional information is essential in the effective monitoring and management of such complex challenges in the environment.The Earth Observation(EO)systems,including optical sensors,radar sensors,Light Detection and Ranging(LiDAR)sensors,thermal sensors,Unmanned Aerial Vehicle(UAV)sensors,and in-situ sensors,offer a good coverage of space and time,as well as provide useful information on land,water,and atmospheric processes.But the shortcomings or weaknesses of individual sensors,such as their vulnerability to weather conditions,spectral or spatial resolution,and gaps in time,can tend to limit their ability to provide a complete picture of the environment.One of the solutions has been multi-sensor fusion,which combines heterogeneous data and makes it more accurate,robust,and interpretable.This systematic review analyzes the latest methods of multi-sensor fusion,which are machine learning,deep learning,probabilistic models,and hybrid approaches,in terms of methodological principles,preprocessing needs,and computational frameworks.Applications in environmental security are highlighted,which include monitoring natural disasters,monitoring of climate and ecosystem,pollution monitoring,monitoring of land use change,and early warning systems.The review also covers evaluation measures,validation plans,and uncertainty measures,where a strict measure of evaluation is vital to making actionable decisions.Lastly,emerging issues,e.g.,data heterogeneity,computational needs,sensor interoperability,and prospects in the future,e.g.,AI-based adaptive fusion,UAVs and Internet of Things(IoT)integration,and scalable cloud-based systems,are discussed.The synthesis has highlighted the transformational capability of multi-sensor EO in terms of improving the environment in the context of environmental security and sustainable management. 展开更多
关键词 Earth Observation Environmental Security Multi-Sensor Fusion Remote Sensing Data Integration
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Research on the Engineering Implementation of UAV Attitude Estimation Based on Multi-Sensor Fusion 认领 引用
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作者 Qi Li Jianbo Zhou 《Journal of Electronic Research and Application》 2026年第2期40-46,共7页
Aiming at the practical problems such as insufficient accuracy and poor robustness when a single sensor is used for UAV attitude estimation,this paper proposes a multi-sensor fusion algorithm based on the Extended Kal... Aiming at the practical problems such as insufficient accuracy and poor robustness when a single sensor is used for UAV attitude estimation,this paper proposes a multi-sensor fusion algorithm based on the Extended Kalman Filter(EKF).With the Inertial Measurement Unit(IMU)as the core sensing component,the scheme fuses Global Positioning System(GPS)and magnetometer data to construct a 16-dimensional state space model,realizing the joint solution of UAV attitude,velocity,and position information.Experimental verification shows that in static tests,the Root Mean Square Error(RMSE)of pitch and roll angle estimation errors is significantly reduced;in dynamic test scenarios,the attitude tracking accuracy is significantly improved compared with the traditional complementary filter algorithm.On this basis,this paper further builds a modular experimental platform for vocational education practice,decomposing the algorithm implementation process into four core teaching modules:sensor data collection,error calibration,state prediction,and measurement update.It provides reusable teaching cases and a complete engineering implementation idea for the curriculum reform of the UAV application technology major in higher vocational colleges. 展开更多
关键词 Multi-sensor fusion Attitude estimation Extended Kalman Filter(EKF) UAV system
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Mobile robot localization algorithm based on multi-sensor information fusion 认领 引用 被引量：9
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作者 WANG Ming-yi HE Li-le +1 位作者 LI Yu SUO Chao 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2020年第2期152-160,共9页
In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm ba... In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm based on multi-sensor information fusion(MSIF)was proposed.In this paper,simultaneous localization and mapping(SLAM)was realized on the basis of laser Rao-Blackwellized particle filter(RBPF)-SLAM algorithm and graph-based optimization theory was used to constrain and optimize the pose estimation results of Monte Carlo localization.The feature point extraction and quadrilateral closed loop matching algorithm based on oriented FAST and rotated BRIEF(ORB)were improved aiming at the problems of generous calculation and low tracking accuracy in visual information processing by means of the three-dimensional(3D)point feature in binocular visual reconstruction environment.Factor graph model was used for the information fusion under the maximum posterior probability criterion for laser RBPF-SLAM localization and binocular visual localization.The results of simulation and experiment indicate that localization accuracy of the above-mentioned method is higher than that of traditional RBPF-SLAM algorithm and general improved algorithms,and the effectiveness and usefulness of the proposed method are verified. 展开更多
关键词 mobile robot simultaneous localization and mapping(SLAM) graph-based optimization sensor fusion
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An Indoor Pedestrian Localization Algorithm Based on Multi-Sensor Information Fusion 认领 引用 被引量：2
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作者 Xiangyu Xu Mei Wang +2 位作者 Liyan Luo Zhibin Meng Enliang Wang 《Journal of Computer and Communications》 2017年第3期102-115,共14页
For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sens... For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi. 展开更多
关键词 Multi-Sensor Fusion Indoor Localization Pedestrian Dead Reckoning (PDR) Particle Filter
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Real-Time Sound Source Localization Method Based on Selective SRP-PHAT and Vision Fusion 认领 引用
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作者 Jinde Huang 《Journal of Electronic Research and Application》 2025年第4期235-241,共7页
Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requi... Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requirements,an innovative high-precision sound source localization method is proposed.This method combines the selective SRP-PHAT algorithm with real-time visual analysis.Its core innovations include using face detection to dynamically determine the scanning angle range to achieve visually guided selective scanning,distinguishing face sound sources from background noise through a sound source classification mechanism,and implementing intelligent background orientation selection to ensure comprehensive monitoring of environmental noise.Experimental results show that the method achieves a positioning accuracy of±5 degrees and a processing speed of more than 10FPS in complex real environments,and its performance is significantly better than the traditional full-angle scanning method. 展开更多
关键词 Sound source localization SRP-PHAT Audio-visual fusion Real-time processing Microphone array
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Research on Vehicle Safety Based on Multi-Sensor Feature Fusion for Autonomous Driving Task 认领 引用 被引量：1
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作者 Yang Su Xianrang Shi Tinglun Song 《Computers, Materials & Continua》 SCIE EI 2025年第6期5831-5848,共18页
Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhan... Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving. 展开更多
关键词 Multi-sensor fusion autonomous driving feature selection attention mechanism reinforcement learning
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OKPS: A Reactive/Cooperative Multi-Sensors Data Fusion Approach Designed for Robust Vehicle Localization 认领 引用
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作者 Adda Redouane Ahmed Bacha Dominique Gruyer Alain Lambert 《Positioning》 2016年第1期1-20,共20页
This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and... This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle. 展开更多
关键词 Localization Mobile Robotic Extended Kalman Filter Particle Swarm Optimization Particle Filter Data Fusion Vehicle Positioning GPS
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Robust RGB-D Camera and IMU Fusion-based Cooperative and Relative Close-range Localization for Multiple Turtle-inspired Amphibious Spherical Robots 认领 引用 被引量：12
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作者 Huiming Xing Liwei Shi +5 位作者 Kun Tang Shuxiang Guo Xihuan Hou Yu Liu Huikang Liu Yao Hu 《Journal of Bionic Engineering》 SCIE EI CSCD 2019年第3期442-454,共13页
In the narrow, submarine, unstructured environment, the present localization approaches, such as GPS measurement, dead?rcckoning, acoustic positioning, artificial landmarks-based method, are hard to be used for multip... In the narrow, submarine, unstructured environment, the present localization approaches, such as GPS measurement, dead?rcckoning, acoustic positioning, artificial landmarks-based method, are hard to be used for multiple small-scale underwater robots. Therefore, this paper proposes a novel RGB-D camera and Inertial Measurement Unit (IMU) fusion-based cooperative and relative close-range localization approach for special environments, such as underwater caves. Owing to the rotation movement with zero-radius, the cooperative localization of Multiple Turtle-inspired Amphibious Spherical Robot (MTASRs) is realized. Firstly, we present an efficient Histogram of Oriented Gradient (HOG) and Color Names (CNs) fusion feature extracted from color images ofTASRs. Then, by training Support Vector Machine (SVM) classifier with this fusion feature, an automatic recognition method of TASRs is developed. Secondly, RGB-D camerabased measurement model is obtained by the depth map In order to realize the cooperative and relative close-range localization of MTASRs, the MTASRs model is established with RGB-D camera and IMU. Finally, the depth measurement in water is corrected and the efficiency of RGB-D camera for underwater application is validated. Then experiments of our proposed localization method with three robots were conducted and the results verified the feasibility of the proposed method for MTASRs. 展开更多
关键词 vision localization bio-inspired robots RGB-D camera histogram of oriented gradient and color names fusion feature Co operative and Relative Localization (CRL)
Cloned s-Lap Gene Coding Area, Expression and Localization of s-Lap/GFP Fusion Protein in Mammal Cells 认领 引用
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作者 SONGYi-shu SONGZhi-yu +4 位作者 LIHong-jun WuYin BAOYong-li TANDa-peng LIYu-xin 《Chemical Research in Chinese Universities》 SCIE EI CAS 2005年第3期298-300,共3页
s-Lap is a new gene sequence from pig retinal pigment epithelial(RPE) cells, which was found and cloned in the early period of apoptosis of RPE cells damaged with visible light. We cloned the coding area sequence of t... s-Lap is a new gene sequence from pig retinal pigment epithelial(RPE) cells, which was found and cloned in the early period of apoptosis of RPE cells damaged with visible light. We cloned the coding area sequence of the novel gene of s-Lap and constructed its recombinant eukaryotic plasmid pcDNA3.1-GFP/s-lap with the recombinant DNA technique. The expression and localization of s-lap/GFP fusion protein in CHO and B_~16 cell lines were studied with the instantaneously transfected pcDNA3.1-GFP/s-lap recombinant plasmid. ~s-Lap/GFP fusion protein can be expressed in CHO and B_~16 cells with a high rate expression in the nuclei. 展开更多
关键词 s-Lap gene Fusion protein Mammal cell Expression Localization
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A survey on Ultra Wide Band based localization for mobile autonomous machines 认领 引用 被引量：1
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作者 Ning Xu Mingyang Guan Changyun Wen 《Journal of Automation and Intelligence》 CSCD 2025年第2期82-97,共16页
The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide... The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines. 展开更多
关键词 Ultra Wide Band Localization Mobile autonomous machines Error mitigation Optimization Sensor fusion
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Digital twin intersection based on roadside multi-sensor data fusion 认领 引用
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作者 Yuhang Wang Yanzhan Chen Liang Zheng 《Digital Transportation and Safety》 2025年第4期242-250,共9页
Digital twin technology is pivotal in the advancement of smart cities and autonomous driving due to its unique capabilities in virtual-reality integration,interactive control,and predictive analysis.The primary enable... Digital twin technology is pivotal in the advancement of smart cities and autonomous driving due to its unique capabilities in virtual-reality integration,interactive control,and predictive analysis.The primary enabler for achieving advanced transportation digital twins lies in enhancing environmental sensing capabilities,with multi-sensor data fusion emerging as a widely adopted strategy to improve sensing performance.However,existing research has predominantly focused on onboard systems,leaving roadside sensor deployment and roadside multi-sensor data fusion strategies insufficiently explored.Recognizing the potential advantages of roadside sensor systems,such as broader sensory field coverage and reduced occlusion.This study investigates the integration of roadside multi-sensor data fusion with digital twin technology in the transportation domain.Consequently,this paper introduces an innovative intersection digital twin system developed through a simulation-based approach,leveraging roadside multi-sensor data late fusion.The Car Learning to Act-Simulation of Urban Mobility(CARLA-SUMO)co-simulator acts as a data generation platform,synchronously producing RGB images and Light Detection and Ranging(LiDAR)point clouds with spatiotemporal consistency.For object detection,we employ the You Only Look Once version 5(YOLOv5)and PointPillars algorithms.Then,a decision-level fusion strategy is proposed to integrate these heterogeneous sensor outputs into a cohesive roadside digital twin system.Experimental results demonstrate that YOLOv5 and PointPillars achieve a mean Average Precision(mAP)of approximately 90%and 60%,respectively.Moreover,the detection frequency of both detectors is well-suited to the dynamic nature of intersection traffic,and the fusion strategy synergistically exploits the complementary advantages of heterogeneous sensors to enhance overall system performance.This research contributes to the field by facilitating low-cost autonomous driving simulation tests and enabling the reconstruction of intersections using roadside digital twin technology,with significant implications for vehicle-road coordination and traffic management. 展开更多
关键词 Digital twin CARLA-SUMO co-simulator Multi-sensor data fusion YOLOv5 PointPillars
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Consistent batch fusion for decentralized multi-robot cooperative localization 认领 引用 被引量：1
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作者 Ning Hao Fenghua He +1 位作者 Yu Yao Yi Hou 《Control Theory and Technology》 EI CSCD 2024年第4期638-651,共14页
This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate s... This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner.To address this problem,we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously.This method can improve both the accuracy and computational efficiency of cooperative localization.To reduce communication costs and reliance on connectivity,we do not maintain the inter-robot state correlations.Instead,we adopt a covariance intersection(CI)technique to design an upper bound that replaces unknown joint correlations.We propose an optimization method to determine a tight upper bound for the correlations in the joint update.The consistency and convergence of our proposed algorithm is theoretically analyzed.Furthermore,we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency. 展开更多
关键词 Multi-robot cooperative localization Decentralized fusion Consistency Covariance intersection
An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion 认领 引用
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作者 Haoyu Yang Yuanshuo Wang +1 位作者 Dongchen Li Tiancheng Li 《Journal of Beijing Institute of Technology》 EI CAS 2022年第6期570-583,共14页
In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according t... In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios.The FFP method fuses the pedestrian dead reckoning(PDR)estimation to solve the moving target localization problem.We also introduce auxiliary parameters to estimate the target motion state.Subsequently,we can locate the static pedestrians and track the the moving target.For the case study,eight access stationary points are placed on a bookshelf and hypermarket;one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf.We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor,weighted k-nearest neighbor,pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter,unscented Kalman filter and particle fil-ter(PF).The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors,espe-cially in the 3D scenarios.Simulation results corroborate the effectiveness of our proposed approach. 展开更多
关键词 3D indoor localization fingerprint fusion positioning time-difference of arrival pedestrian dead reckoning received signal strength
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A Robust Hybrid Multisource Data Fusion Approach for Vehicle Localization 认领 引用 被引量：2
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作者 Adda Redouane Ahmed Bacha Dominique Gruyer Alain Lambert 《Positioning》 2013年第4期271-281,共11页
In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low... In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost GPS, an INS, an Odometer and a Steering wheel angle encoder. The OKS is developed addressing the challenge of managing reactivity and robustness during a real time ego-localization process. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. In these situations, the balance between reactivity and robustness concepts is crucial. The OKS filter represents an intelligent cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO). It combines advantages coming from two filters: Particle Filter (PF) and Extended Kalman filter (EKF). The OKS is tested using real embedded sensors data collected in the Satory’s test tracks. The OKS is also compared with both the well-known EKF and the Particle Filters (PF). The results show the efficiency of the OKS for a high dynamic driving scenario with damaged and low quality GPS data. 展开更多
关键词 Localization Mobile Robotic Kalman Filter EKF Particle Swarm Optimization PSO Particle Filter Data Fusion Vehicle Positioning Navigation GPS
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Multi-sensor measurement and data fusion technology for manufacturing process monitoring:a literature review 认领 引用 被引量：27
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作者 Lingbao Kong Xing Peng +2 位作者 Yao Chen Ping Wang Min Xu 《International Journal of Extreme Manufacturing》 SCIE EI CAS 2020年第2期1-27,共27页
Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities i... Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper. 展开更多
关键词 multi-sensor data fusion process monitoring additive manufacturing laser melting
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Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor 认领 引用 被引量：22
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作者 Jin-chuan SHI Yan REN +1 位作者 He-sheng TANG Jia-wei XIANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2022年第4期257-271,共15页
Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnos... Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions. 展开更多
关键词 Hydraulic directional valve Internal fault diagnosis Weighted multi-dimensional features Multi-sensor information fusion
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