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检索条件"任意字段=Conference on Radar Sensor Technology and Data Visualization"

共 4471 条记录，以下是151-160 订阅

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Autonomous Delivery Vehicle System Based on Multi-sensor data Fusion

Autonomous Delivery Vehicle System Based on Multi-Sensor Dat...

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2024 International conference on Intelligent Robotics and Automatic Control

作者： Lei, Runyu Tang, Longbin Guo, Jiaxin Sun, Jie Bu, Qinglei Natl Univ Singapore Suzhou Res Inst Suzhou Peoples R China Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou Peoples R China

ISBN: (纸本)9798350389814;9798350389807

With the rapid advancement of science and technology, people's yearning for a more convenient life has gradually become concrete, which has given rise to a huge logistics industry chain. At the same time, the ever-increasing labor costs have caused an urgent need for autonomous delivery vehicles. This paper presents the design and integration of an autonomous vehicle system for efficient indoor parcel delivery, addressing the challenges of navigating dynamic indoor environments. This vehicle system leverages a multi-sensor approach, integrating LiDAR, radar, Inertial Measurement Unit (IMU), GPS, and odometer data to provide comprehensive environmental perception and precise localization, which will contribute to the field by providing a practical application of autonomous vehicle technology in a constrained environment, offering a solution for the logistics industry's indoor delivery challenges.

关键词： LiDAR SLAM Autoware.Universe Path Planning Multi-sensor data Fusion

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Application of data Fusion Algorithms in the data Processing of Intelligent Greenhouses 1

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13th International conference on Frontier Computing, FC 2023

作者： Li, Jinguang Li, Dan College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin City China Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin Jilin City China

ISBN: (数字)9789819992997

ISBN: (纸本)9789819992980

Greenhouses can ensure the normal growth of crops in extreme environments. With the popularization of social intelligence, how to build an efficient and accurate environmental monitoring system to ensure that crops have suitable growth conditions is a top priority. In order to effectively solve the problem of data missing and large data errors caused by sensor collection in intelligent greenhouses under extreme environments, this paper uses data preprocessing and multi-sensor fusion algorithms to ensure the accuracy of data. The effectiveness of the data fusion algorithm is verified through data transmission experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Crops

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2025 4th International conference on Electronic Information and Communication Engineering, EICE 2025

2025 4th International Conference on Electronic Information ...

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4th International conference on Electronic Information and Communication Engineering, EICE 2025

The proceedings contain 27 papers. The topics discussed include: a pruned wavelet neural network noise optimization method for fiber optic gyroscope;an improved NSGA-II algorithm for multi-sensor scheduling optimization;research on automated annotation technology for radar range angle-doppler data based on visual supervision mechanism;multi-UAV deployment in complex mountain marathon: two tier relay and multi-objective genetic optimization;mobile sink path planning for data collection in three dimensional wireless sensor network;design of high voltage envelope tracking power supply based on multilevel architecture;a dynamic STC attenuation strategy based on environmental perception;and an automatic balancing algorithm for interface timing skew.

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Research on Environmental Perception Based on YOLOv8 and LiDAR data Fusion 4

Research on Environmental Perception Based on YOLOv8 and LiD...

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4th IEEE International conference on Information technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Luo, ZhenHong Li, HuiLing Li, Ting Liu, Jie Guangxi Vocational Normal University School of Computer and Information Engineering Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (纸本)9798350363586

To enhance the sensing capability of driverless cars in complex dynamic scenarios, a fusion method of radar and depth camera is emphasized and a technical framework for integrating multi-source sensors is proposed. A YOLOv8 neural network detection model combined with a self-attention network structure is first used to achieve correct recognition and depth estimation of vehicles and pedestrians, then further distance data of dynamic environment is acquired by M10P LiDAR, and finally information from D435i camera and radar information are fused by matrix projection. In the experiments, the conventional YOLOv8 model was compared to the YOLOv8 model enhanced with self-attention, the camera and LIDAR measurements were evaluated against the fusion results. The experimental results demonstrate that the new model significantly improves the confidence and stability of target detection, while the fused distance measurements are more accurate than those from individual sensors. © 2024 IEEE.

关键词： sensor data fusion

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radar-Lidar Fusion for Object Detection by Designing Effective Convolution Networks 26

Radar-Lidar Fusion for Object Detection by Designing Effecti...

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IEEE 26th International conference on Intelligent Transportation Systems (ITSC)

作者： Munir, Farzeen Azam, Shoaib Kucner, Tomasz Kyrki, Ville Jeon, Moongu Aalto Univ Dept Elect Engn & Automat Espoo Finland Finnish Ctr Artificial Intelligence Espoo Finland Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju South Korea

ISBN: (纸本)9798350399462

Object detection is a core component of perception systems, providing the ego vehicle with information about its surroundings to ensure safe route planning. While cameras and Lidar have significantly advanced perception systems, their performance can be limited in adverse weather conditions. In contrast, millimeter-wave technology enables radars to function effectively in such conditions. However, relying solely on radar for building a perception system doesn't fully capture the environment due to the data's sparse nature. To address this, sensor fusion strategies have been introduced. We propose a dual-branch framework to integrate radar and Lidar data for enhanced object detection. The primary branch focuses on extracting radar features, while the auxiliary branch extracts Lidar features. These are then combined using additive attention. Subsequently, the integrated features are processed through a novel Parallel Forked Structure (PFS) to manage scale variations. A region proposal head is then utilized for object detection. We evaluated the effectiveness of our proposed method on the Radiate dataset using COCO metrics. The results show that it surpasses state-of-the-art methods by 1.89% and 2.61% in favorable and adverse weather conditions, respectively. This underscores the value of radar-Lidar fusion in achieving precise object detection and localization, especially in challenging weather conditions.

关键词： radar object detection sensor fusion attention

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Board 103: Solar-Powered Car Speed radar Measurement, Display, and Logging System

Board 103: Solar-Powered Car Speed Radar Measurement, Displa...

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2023 ASEE Annual conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Ciko, Anthony Cortes, Yave Jones, Josiah Andrew Sohail, Jimmy Blinder, Alex Azizi, Mohsen New Jersey Institute of Technology United States

Studies have shown that there is a 10% fatality rate when a pedestrian is struck by a vehicle moving at the speed of 20 (mph), and the rate scales up to 90% at the speed of 40 (mph). Moreover, the residential areas with radar speed monitors are shown to be safer in terms of accident probability. Motivated by these statistics, in this National Science Foundation sponsored senior design project a speed radar system is designed and developed. The components, functionalities, and objectives of the project are listed as follows: (i) A camera will detect and identify a vehicle and distinguish it from other objects;(ii) a radar sensor will measure the speed of the vehicle;(iii) a microprocessor (Raspberry Pi) will acquire the speed data, send it to the display, and analyze and log it in a server;and (iv) a stand-alone solar Photovoltaic system will provide electrical power to and guarantee the continuous operation of the entire system. This senior design project was conducted by a group of undergraduate students in the electrical and computer engineering technology program at New Jersey Institute of technology. © American Society for Engineering Education, 2023.

关键词： Solar energy

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3D object detection in autonomous driving: Matching point cloud clustering with neural network classification 8

3D object detection in autonomous driving: Matching point cl...

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8th International conference on Traffic Engineering and Transportation System, ICTETS 2024

作者： Lu, Yuxiao Kunming Shipborne Equipment Research & Test Center Shilipu Road No.2999 Kunming650051 China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

This paper proposes a novel 3D object detection method based on object matching for autonomous driving, which combines point cloud data and image processing. The method utilizes point cloud clustering to determine the location and size of objects, while a neural network classifies objects based on image data. Object detection is then achieved through the matching of these results. Experimental testing of the algorithm demonstrates the effectiveness of this fusion approach. © 2024 SPIE.

关键词： Object detection Point clouds radar Neural networks Cameras radar sensor technology Autonomous driving Mahalanobis distance

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A Dynamic Short-range Animal Migration Forecast Model Based on Weather radar Network

A Dynamic Short-range Animal Migration Forecast Model Based ...

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IET International radar conference 2023, IRC 2023

作者： Liu, Xuan Cui, Kai Hu, Cheng Wang, Rui Mao, Huafeng Wu, Dongli Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China Meteorological Observation Centre of China Meteorological Administration Beijing100081 China

ISBN: (纸本)9781839539954

Predicting aerial animal migration has great implications for biological research and conservation, and agricultural production. Animal migration is deeply coupled with spatio-temporal and meteorological factors, however, the existing forecast model is only based on the spatio-temporal features of animal migration and the impact of meteorological factors on migration is not considered. In this paper, we propose a dynamic forecast model to forecast airborne migration from the perspective of the influence of wind field data on migration and makes prediction with the help of neural network. Specifically, we calculate the transition probability matrix using the wind data and the migration spatial field to represent the migration dynamic model, and integrated this model into deep neural network. We use the data of China's weather radar network to verify that our model outperforms the competing methods. In addition, the cause of prediction error at peak value is analysed by visualization results. In future applications, more meteorological factors should be considered to improve the representation of dynamic models. © The Institution of Engineering & technology 2023.

关键词： Wind forecasting

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Power Operation and Maintenance data Panoramic Perception System Based on GIS and Big data Analysis Technologies 2

Power Operation and Maintenance Data Panoramic Perception Sy...

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2nd International conference on Artificial Intelligence and Automation Control, AIAC 2024

作者： Ju, Moxin Liu, Lijun Ding, Qiang Wang, He Tao, Yanrong Hu, Sen State Grid Jilin Marketing Service Center Changchun China State Grid Siping Power Supply Company Changchun China

ISBN: (纸本)9798331529147

With the advancement of digital transformation in the power industry, traditional power operation and maintenance management models can no longer meet the needs for real-time monitoring and precise decision-making. This paper proposes a power operation and maintenance data panoramic perception technology based on Geographic Information Systems (GIS) and big data analysis, aimed at improving the intelligence level of power operation and maintenance and achieving refined management of power facilities. The technology integrates spatial data, smart sensor data, dynamic visualization dashboards, and stream data processing techniques to build a comprehensive power operation and maintenance management platform, enabling seamless collaboration and information sharing among data collection, edge computing, data analysis, and GIS systems. © 2024 IEEE.

关键词： data assimilation

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Research on Human Joint Motion Attitude Capture System Based on Inertial sensor 2

Research on Human Joint Motion Attitude Capture System Based...

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2nd International conference on Intelligent Equipment and Special Robots, ICIESR 2023

作者： Lang, Yanxin Zhang, Chao Zhong, Peisi Li, Tianping Liu, Mei Sun, Weili Advanced Manufacturing Technology Research Center Shandong University of Science and Technology Qingdao266590 China College of Mechanical and Electronic Engineering Shandong University of Science and Technology Qingdao266590 China College of Intelligent Manufacturing Qingdao Huanghai University Qingdao266555 China

ISBN: (纸本)9781643685144

This manuscript introduces a novel framework and algorithm that leverages inertial sensors to capture and optimize data on human joint motion posture, thereby enhancing the accuracy and stability of contemporary motion capture technology. By wirelessly transmitting nine-axis motion data from joint-worn inertial sensor nodes to a host computer, real-time visualization of motion attitude data is achieved through software. Moreover, the acquired data is effectively denoised through a two-stage extended Kalman filter algorithm. Subsequently, the feasibility of capturing motion attitude using the inertial nodes, as well as the effectiveness of the proposed algorithm, is empirically validated through single-node experiments, substantially elevating the overall stability of the system. © 2024 The Authors.

关键词： Extended Kalman filters

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