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

共 5986 条记录，以下是221-230 订阅

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Multi-sensor Fusion Localization Algorithms for Intelligent Agricultural Unmanned Vehicles in Complex Environments

Multi-Sensor Fusion Localization Algorithms for Intelligent ...

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2024 International conference on Innovative Design and Intelligent Manufacturing, IDIM 2024

作者： Yang, Kaihang Wang, Xiaodong Zhao, Hongyang Wen, Haojie Qin, Jinyan Wang, Ziheng Liaoning Institute of Science and Technology Benxi117004 China

ISBN: (纸本)9781643685427

In order to solve the problems of short-time data loss, low positioning accuracy and asynchronous sensor frequency of agricultural unmanned vehicles, the application of multi-sensor fusion positioning algorithm of intelligent agricultural unmanned vehicles in complex environments was proposed. This paper uses laser radar, IMU and wheel odometer to obtain positioning information, and proposes a combined data fusion method based on extended Kalman filter and complementary fusion. Firstly, the initial positioning data is preprocessed by S-G filtering algorithm, and the positioning data fusion of IMU and wheel odometer sensor is realized by using the extended Kalman filtering fusion algorithm to obtain the fusion data 1;Then, the fusion data 1 and the laser radar are fused using the complementary fusion algorithm to obtain the fusion positioning data 2. The fusion data 1 carries out real-time correction for the laser radar to solve the displacement deviation of frequency asynchrony, thus significantly improving the positioning accuracy. Finally, the Gazebo simulation platform is used to build the agricultural unmanned vehicle model and set the basic parameters of the sensor to verify the effectiveness and stability of the algorithm. The experimental results show that the data fusion algorithm improves the positioning accuracy and stability of the nonlinear sensor, and the average positioning error is within 8 cm. Conclusion: The algorithm is effective for improving the positioning accuracy of agricultural unmanned vehicles, and has important application value for agricultural unmanned vehicles combined with low-cost sensors. © 2024 The Authors.

关键词： Extended Kalman filters

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RF-Hardening of Small UAS for Mitigation of HIRF Impacts 28

RF-Hardening of Small UAS for Mitigation of HIRF Impacts

引用

conference on radar sensor technology XXVIII

作者： Lau, Morgan Suarez, Hernan Zhang, Yan Rockee Reynolds, Kegan Boyle, Steve Univ Oklahoma Intelligent Aerosp Radar Team Sch Elect & Comp Engn Norman OK 73019 USA Univ Oklahoma Adv Radar Res Ctr Norman OK 73019 USA Essential Aero Inc 4465 Granite DrSte 700 Rocklin CA 95677 USA

ISBN: (纸本)9781510674158;9781510674141

The survivability of small Unmanned Aerial Systems (sUAS) in high-intensity electromagnetic environments (HIRF) is a significant characterization that determines the capabilities of UAS in critical operation situations. The existing small UAS system designs do not consider the complexity of adversary electromagnetic environments, such as Radio Frequency (RF) interference, electromagnetic interference (EMI), Counter-UAS (C-UAS) electromagnetic attack (EA), and other nearby sources of noises in spectrums.(1-8) This study aims to determine the feasible approaches that support the "hardening" of the existing S-UAS to counter or mitigate the impacts of HIRF and evaluate such improvements in both laboratory environments and flight tests. The first step is to identify the weak spots of the existing flight vehicles, from the propeller, motors, and controllers, as well as the internal electronics, especially the RF radios and power supplies. The second step is to emulate the HIRF environment we expect in multiple domains (such as air, sea, or ground airport) in laboratory environments. We are introducing a lab-based emulation testbed with higher power and better configurations than the previous work and installed it for the new emulation experiments. Thirdly, we perform careful power level measurements and calibrations and compare them to the theoretical models. The system setup and size focus on the S-band (near Wi-Fi band) radio links, electronic components, onboard GPS, and navigational sensors. At the same time, the same approach can be easily extended to other frequencies. From this study, we introduced three levels of mitigation measures. (1) Minimal protection. (2) Shielding solution. (3) Shielding and EMI filtering solution. We demonstrated how these levels of mitigation solutions affect the risk levels of survivability in the HIRF environments near high-power radars.

关键词： RF Stealth C2 link HIRF RFI EMI Mitigation

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Enhancing the Survivability of UAV using Multi-sensor Data Fusion

Enhancing the Survivability of UAV using Multi-Sensor Data F...

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2024 IEEE International conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Hizkial, Shiba Azam, Farooque School of Computer Science and Engineering Reva University Bengaluru India

ISBN: (纸本)9798350382693

sensor data fusion helps to derive more specific inferences than what could be achieved using a single independent sensor. Multi-sensor Data Fusion (MSDF) technology enhance the capability of improving accuracy, information and efficiency and is the right choice in improving the estimate. Data fusion is the process of combining information from a number of different sources to provide a robust and complete description of an environment of process of interest. Real-time fusion of data collects from a variety of radars that acquire information from multiple perspectives provides a more accurate picture of the target vehicle than any single radar or group of radars operating independently. This paper discusses the implementation of detection of the position of the Missile using Multi-sensor data Fusion (MSDF) algorithm. The algorithm is overall weighted fusion algorithm which uses property of Kalman filter for data processing and the state estimation, weighted fusion by the least squares method. The simulation with the help of MATLAB is conducted for the evaluation of the performance. The result is compared where the final output after the least square methods using the weights, how the accuracy and robustness is improved by plotting the errors.. © 2024 IEEE.

关键词： Kalman filters

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Autonomous Residual Threshold Detection for Decentralized radar Inertial Odometry in Hostile Environments

Autonomous Residual Threshold Detection for Decentralized Ra...

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2024 International conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Mukherjee, Moumita Banerjee, Avijit Koval, Anton Nikolakopoulos, George Luleå University of Technology Robotics and Ai Group Department of Computer Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350361025

The article introduces an adaptive threshold detection mechanism aimed at enhancing a decentralized radar inertial odometry (RIO) framework, enabling resilient localization in challenging hostile environments. Considering that frequency-modulated continuous-wave (FMCW) radars possess characteristics enabling measurements in challenging indoor and outdoor environments, the fusion of multiple radars in an ensemble configuration, along with an inertial measurement unit (IMU), holds promise in surpassing individual sensor limitations. This approach thereby enhances robust perception. The proposed adaptive autonomous residual threshold mechanism employs a real-time residual analysis to dynamically adjust the sensor fusion process by comparing the variance between two extended Kalman filters. This adaptive approach addresses irregularities in data samples from multiple sensors, thereby enhancing the decentralized smoothing estimator's precision in providing localization while navigating through hostile environments marked by limited visibility, extreme weather, or high interference. Consequently, it contributes to the resilience and adaptability of autonomous systems in real-world scenarios. The proposed framework effectively showcases precise localization through decentralized radar inertial odometry(RIO). © 2024 IEEE.

关键词： Kalman filters

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Autonomous Navigation using Lidar sensor in ROS and GAZEBO 2

Autonomous Navigation using Lidar Sensor in ROS and GAZEBO

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2nd IEEE International conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Subhashini, K. Rathiksha, G. Keerthana, B. Amirthavarshini, P. Sri Sai Ram Engineering College Department of Ece Chennai India

ISBN: (纸本)9798350383287

The field of autonomous navigation has been significantly advanced. Autonomous Vehicle depends on their perception system to obtain critical data pertaining to their immediate environment. This article elucidates the process of developing an autonomous Segway model which uses LIDAR sensor. This Segway can only be used on smooth indoor surfaces and continues on the designated path, dodging obstacles. LiDAR-based autonomous navigation system outlines the development of algorithms for obstacle detection, path planning, and localization. In response to the imperative requirement of safety and precision, LIDAR integration system has been introduced as a supplementary element alongside camera and radar based perception system. Conversely, contemporary practice involves planning most processes through simulations, enabling the anticipation and resolution of future production issues. Therefore, the entire stimulation is done within the Gazebo simulation environment and the entire data communication is managed through ROS (Robot Operating System) which provides a robust platform for implementing in Segway. The robot operating system offers specific configuration options and functionality for a mobile Segway working in autonomous navigation mode. There are phases for making a map, localizing it, and navigating it. The settings of the simulation control parameters as well as the Segway model itself have been optimized and fine-tuned through experiments. © 2024 IEEE.

关键词： Optical radar

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Research and implementation of a multi-source sensing data posture fusion display system 24

Research and implementation of a multi-source sensing data p...

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5th International conference on Computer Science and Management technology, ICCSMT 2024

作者： Yan, Lintong Chen, Xiaobo Liu, Yijia Li, Zhiyong Information Science Academy of China Electronics Technology Group Corporation Beijing China Beijing Institute of Technology Beijing China 66242 Units of the Chinese People's Liberation Army Xilinguole Inner Mongolia China

ISBN: (纸本)9798400709999

Aiming at the problems such as vector map, image, DEM, inclined photography and other basic geographic information data and real-life photographs, surveillance video, drone video, laser point cloud, SAR radar images and other high-level geographic information data format differences, data management complexity, and difficult to integrate the situation, this paper centers on the management of multi-source sensor data and the integrated display of real-life situation to carry out research in two aspects, the use of large amounts of data Load scheduling technology, monitoring video automatic alignment technology, multi-source heterogeneous data lightweight processing technology, breakthrough multi-source sensing data situational fusion display technology, build multi-source sensing data situational fusion display system, to solve the multi-source heterogeneous data unified management, fusion processing, situational display and other issues, in order to realize the large-scale massive three-dimensional model data multi-source data management and two-three-dimensional visualization of the situational display to lay the foundation and provide the tools. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： radar imaging

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Enhancing Millimeter-Wave radar Object Detection with a 3D Transformer Module on Range-Azimuth Tensors

Enhancing Millimeter-Wave Radar Object Detection with a 3D T...

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IET International radar conference 2023, IRC 2023

作者： Wu, Han Zhao, Yinan Song, Mingqi Han, Tianhao Feng, Xiang School of Communication Engineering Hangzhou Dianzi University Hangzhou China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Information Science and Engineering Harbin Institute of Technology Weihai China

ISBN: (纸本)9781839539954

The millimeter-wave (mmWave) radar has been a robust and cost-effective sensor in the perception system. However, mmWave radar object detection is still a great challenge because radar signals contain far less semantic information than RGB images and are difficult to decipher directly by a human observer. Considering this, this letter presents an end-to-end network termed as 3D-Transformer-CNN (3D-T-CNN) for mmWave radar object detection using Range-Azimuth (RA) tensors. In this method, a non-local module transformer based on multi-head self-attention is introduced into a 3D vanilla convolutional neural network (CNN), which can make the best use of local and global features of sequential RA tensor frames. The experimental results show that the 3D-T-CNN significantly improves the accuracy of radar object detection, which achieves state-of-the-art performance. © The Institution of Engineering & technology 2023.

关键词： Tensors

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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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Human Activity Recognition Based on radar and Video Surveillance sensor Fusion

Human Activity Recognition Based on Radar and Video Surveill...

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2023 IEEE Ural-Siberian conference on Biomedical Engineering, Radioelectronics and Information technology, USBEREIT 2023

作者： Lobanova, Vera Bezdetnyy, Dmitry Anishchenko, Lesya Bmstu Remote Sensing Laboratory Moscow Russia Bmstu Biomedical Engineering Department Moscow Russia

ISBN: (纸本)9798350336054

Activity monitoring is an actual problem in the prevention of life-threatening conditions. This question is especially important for people over 65 who fall at least once a year. Unstable gait is one of the predictors of falls and the future progression of cognitive diseases. Hence, abnormal gait detection can potentially be a useful tool for the prevention of falls and the diagnosis of cognitive impairment. In this paper, a sensor fusion approach for human activity recognition is presented. radar and video surveillance data of eight volunteers (19-37 years old) were used for training the deep learning model for the binary classification problem (normal/unstable gait). radar data were processed using a wavelet transform, and a pre-trained AlexNet neural network was fitted using a training dataset. Information about skeletal key points was extracted from video frames by means of a BlasePose neural network and used for training the recurrent neural network with long-short-term memory units. Then, a sensor fusion approach at the classification level was applied. The accuracy and Cohen's kappa of the radar-only model were 0.931 and 0.859, respectively. The accuracy and Cohen's kappa of the video-only model were 0.966 and 0.930, respectively. Finally, the accuracy and Cohen's kappa for the sensor fusion-based model were 0.931 and 0.859, respectively. The results of our work can be potentially used for the design of the system for human activity recognition in the smart home technology paradigm. © 2023 IEEE.

关键词： Accident prevention

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Design of New Traffic System YOLO-LIO: Light-Traffic Intercept and Observation 13

Design of New Traffic System YOLO-LIO: Light-Traffic Interce...

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2024 International conference on radar Antenna Microwave Electronics and Telecommunications

作者： Muwardi, Rachmat Zhang, Haiyang Gao, Hongmin Yunita, Mirna Wang, Yanxi Yuliza Univ Mercu Buana Dept Elect Engn Jakarta Indonesia Beijing Inst Technol Sch Opt & Photon Beijing Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Inst Technol Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350389937;9798350389920

Vehicle detection is an area of active development aimed at enhancing driving safety and ensuring compliance with traffic regulations. Despite ongoing efforts, accidents and traffic violations continue to pose significant challenges, leading to disruptions in driving. In response to these issues, the author aims to develop a more efficient traffic management system to improve driver organization and driving behavior. To achieve this, the author proposes using YOLO-LIO as the neural network of choice for the Traffic System. The effectiveness of YOLO-LIO was evaluated using three datasets: the Montevideo Audio and Video Dataset (MAVD), the GARM Road-Traffic Monitoring dataset (GRAM-RTM), and a custom dataset created by the author. The results highlight the superior performance of YOLO-LIO in vehicle detection tasks, achieving accuracy rates of 99.02% on the MAVD dataset, 99.55% on the GRAM-RTM dataset, and 99.32% on the custom dataset. This demonstrates the model's high effectiveness across various datasets. Additionally, the author conducted experiments incorporating OCR technology with the YOLO-LIO algorithm in the Traffic System. The system achieved an accuracy of 80.21% in vehicle number detection, demonstrating its effectiveness. This result reflects the overall performance of the entire system process, from data input to the final detection output, ensuring a comprehensive and accurate detection mechanism. Compared to other algorithms such as YOLOv3 + OCR, YOLOv4, and Faster R-CNN, YOLO-LIO + OCR, they have exhibited significantly better performance. These promising results highlight the potential of YOLO-LIO in creating a robust Traffic System that can significantly enhance road safety and traffic regulation compliance.

关键词： Object Detection YOLO Image Processing CNN Real-Time sensor

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