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

共 6000 条记录，以下是381-390 订阅

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Enhanced Lane Keeping Assistance through IoT sensor Fusion and Deep Learning 2

Enhanced Lane Keeping Assistance through IoT Sensor Fusion a...

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2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Pachpol, Shreyash Vignesh, C. Chandra Jeslin, J. Gnana Kumar, B. V. Praveen Viswanathan, C. Srinivasan, C. Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India RAIK Coll Engn & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Usha Rama Coll Engn & Technol Dept Comp Sci & Engn Vijayawada Andhra Pradesh India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Univ Saveetha Sch Engn Saveetha Inst Med & Tech Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

This research introduces a novel method for improving Lane Keeping Assistance (LKA) systems via the use of Convolutional Neural Networks (CNNs) and Internet of Things (IoT) sensor fusion. The proposed system compiles information about the vehicle's environment from various sensors, such as cameras, LiDAR, radar, and GPS. Using sensor fusion methods, the system enhances its ability to recognize lane markers, obstructions, and other important road elements by combining the capabilities of each sensor modality. An important part of the system is using CNNs to analyze visual sensor input in real-time. To help drivers stay in their lanes, the system uses CNNs trained on massive datasets of tagged images to identify automobiles, lane markers, and other objects. The system learns from its surroundings and the driver's actions in real time. It can adjust to different roads and driving styles, providing individualized support while keeping everyone safe. The proposed method improves LKA systems' accuracy, dependability, and adaptation to different driving situations, as shown experimentally. Due to cutting-edge vehicle safety technology that combines IoT sensor fusion with CNN-based deep learning, drivers may now travel with more assurance and serenity.

关键词： Automotive Safety Real-time Analysis Image Recognition sensor Integration Automated Driving Safety Enhancement

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Mutual sensor Task Assignment and Distributed Track Fusion Method for Multi-UAV sensors 8

Mutual Sensor Task Assignment and Distributed Track Fusion M...

引用

8th International conference on Control, Robotics and Cybernetics, CRC 2023

作者： Qin, Guo-Jie Liu, Jinrong Xu, Peng China Academy of Electronics and Information Technology Beijing China China South Industry Academy Beijing China

ISBN: (纸本)9798350330588

In order to realize the effective mutual sensor task assignment of multi-UAV radars and to make them work collaboratively and minimize the tracking error after the distributed Kalman filtering, we proposed an architecture of multi-UAV mutual sensor task assignment and tracking. The mutual task assignment model function is formulated based on the detection probability and the probability of assignment. The distributed Kalman filtering model between multi-UAV radars is formulated based on the basic frame of the distributed Kalman filter with certain optimization criteria. Simulation results illustrating the performance of the proposed methods are also presented, which proves that the effectiveness of proposed multiUAV radar sensor task assignment method. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Improving radar-Camera Fusion-based 3D Object Detection for Autonomous Vehicles 12

Improving Radar-Camera Fusion-based 3D Object Detection for ...

引用

12th International conference on System Engineering and technology (ICSET)

作者： Kurniawan, Irfan Tito Trilaksono, Bambang Riyanto Inst Teknol Bandung Sch Elect Engn & Informat Bandung Indonesia

ISBN: (纸本)9798350319835

Object detection is essential for autonomous driving as it provides knowledge of the state of the surrounding objects. Aside from the objects' 3D bounding boxes, 3D object detection also estimates the objects' velocity and attribute. radar-camera fusion-based object detection methods have the potential to deliver accurate and robust results even in harsh conditions while being more affordable than lidar-based solutions. However, current radar-camera fusion-based methods are still significantly outperformed by their lidar-based counterparts, even when radar has access to direct velocity measurement which lidar lacks. In this work, we propose a feature-level radar-camera fusion-based object detection architecture. Our architecture associates preliminary detections produced by an image-based pipeline with radar detection clusters using a frustum-based association mechanism. By using radar detection clusters instead of individual points, we can extract features that allow the model to use the geometry of the clusters to refine the preliminary detections. We evaluated our architecture on the nuScenes dataset and achieved a nuScenes detection score (NDS) of 0.465. We also find that our architecture achieved significantly better objects' orientation, velocity, and attribute estimation results compared to other radar-camera fusion-based methods.

关键词： radar camera object detection sensor fusion deep learning

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A Chaotic Sailfish Optimizer for Accurate DOA Estimation in Automotive radar 2

A Chaotic Sailfish Optimizer for Accurate DOA Estimation in ...

引用

2nd International conference on Microwave, Optical and Communication Engineering, ICMOCE 2023

作者： Geetha, P. Nanda, Satyasai Jagannath Yadav, Rajendra Prasad Malaviya National Institute of Technology Department of Electronics and Communication Engineering Rajasthan Jaipur302017 India

ISBN: (纸本)9798350321609

Accurate Direction of Arrival (DOA) estimation has been a challenging problem in communication. In this paper, the DOA estimation in the application of automotive radar is dealt as an optimization problem. A Maximum likelihood estimator is used, and the optimization is carried out by a recently reported Chaotic Sailfish Optimizer (CSFO). The simulation is demonstrated by using two sources, uniform linear sensor array of ten sensors, operating under very high noise conditions at OdB and 5dB SNR. Comparative analysis is demonstrated with the similar model trained with three more optimizer namely: Sailfish optimizer (SFO), Whale Optimizer (WoA), and Particle Swarm Optimizer (PSO). Simulation results reveal the accurate DOA estimation achieved by CSFO with reduced computational time. © 2023 IEEE.

关键词： Automotive radar

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Machine Learning-Based Dielectric Constant Measurement Method

Machine Learning-Based Dielectric Constant Measurement Metho...

引用

2024 IEEE International conference on Computational Electromagnetics, ICCEM 2024

作者： Feng, Yue Li, QiXuan Zhang, Yi Liu, Liangliang Li, Zhuo Nanjing University of Aeronautics and Astronautics Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing211106 China

ISBN: (纸本)9798350383317

Accurately predicting dielectric constants is essential in various fields. However, current microwave measurement methods often fail to utilize sensor data fully. Data-driven artificial neural networks can better identify correlations between output signals and sample features. This study proposes an approach that collects data through model simulations and trains artificial neural networks to predict dielectric constants for test materials more accurately. This research provides valuable insights for practical dielectric constant measurement and prediction applications. © 2024 IEEE.

关键词： Machine learning

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radar and Camera Fusion for Target Detection Based on Attention Enhancement

Radar and Camera Fusion for Target Detection Based on Attent...

引用

2023 International conference on Image, Signal Processing, and Pattern Recognition, ISPP 2023

作者： Zhang, Yiwen Wang, Yuanzheng Dong, Hao Hu, Wen College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics Astronautics Shenzhen Research Institute Nanjing China

ISBN: (纸本)9781510666351

To solve the problem of insufficient representation of radar information in the fusion process, this paper proposes a fusion architecture based on attention enhancement. Considering that the projection area of radar point clouds can provide target's position information, the spatial attention weight is obtained from radar images and then weighted to the camera images to supply the target's position information from radar. At the same time, the transverse velocity, radial velocity and distance of the targets are assigned to each channel of the radar images, and the channel attention mechanism is used to enhance the channel feature of the radar images. We adopt RetinaNet with the backbone of Mobilenetv2 to build the fusion network architecture and perform target detection for radar images and camera images. The result shows that the proposed fusion method performs better than baseline network on nuScenes dataset, the mAP increased by 2.56% and the network is more lightweight. © 2023 SPIE.

关键词： Image enhancement

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Obstacle Collision Warning Using Ultrasonic and LiDAR sensor Fusion 11

Obstacle Collision Warning Using Ultrasonic and LiDAR Sensor...

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11th IEEE Region 10 Humanitarian technology conference, R10-HTC 2023

作者： Gurav, Gayatri Balakrishnan, P. Amrita School of Engineering Department of Electronics and Communication Engineering Coimbatore India Amrita School of Engineering Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350326147

Recently, passenger vehicles were equipped with an Advanced Driver Assistance System (ADAS) feature in assisting drivers to control the vehicle, which gives an alarming signal about possible dangers. Obstacle collision warning (OCW) uses sensors installed in front of the vehicle to detect distance between the front obstacle and the vehicle, to avoid a collision. In common, the types of sensors used in obstacle detection are camera, radar, ultrasonic sensor, and so on. In this paper, fusion of ultrasonic sensor and Light Detection and Ranging (LiDAR) is proposed, which provides extended range detection and increased accuracy. Hardware implementation of ultrasonic sensor and Leddar M16 LiDAR sensor fusion is carried out using an Arduino UNO controller and for, in vehicle communication Controller Area Network (CAN) protocol were used. The outcome of both short and long-distance obstacle detection using ultrasonic and LiDAR are compared. © 2023 IEEE.

关键词： Advanced driver assistance systems

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YOLOv7-SIMAM: An effective method for SAR ship detection 4

YOLOv7-SIMAM: An effective method for SAR ship detection

引用

4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Ning, Tianle Pan, Shuai Zhou, Jian Southwest University of Science and Technology School of Information Engineering Sichuan Mianyang China

ISBN: (纸本)9798350394375

Synthetic Aperture radar (SAR) is an all-weather sensor extensively employed in military investigations, maritime rescue operations, and maritime transport management. However, the accuracy of ship detection in SAR images significantly influences the success of these tasks. To improve ship detection performance, we modify its structure and deploy the attention module on the YOLOv7 network. In deep learning, an attention-based model has better results than a convolution neural network. To solve the problem that embedding attention mechanism into convolutional neural network (CNN) has limited ability to extract spatial features and increased computational amount, we adopted SIMAM attention mechanism not only avoids introducing extra parameters but also concentrates on the feature space distribution across different channels, facilitating the efficient extraction of ship positions in high-resolution SAR images. Experimental results on the publicly measured SAR ship detection dataset (SSDD) demonstrate a 3.3% improvement in accuracy for the proposed SIMAM-YOLOv7 over the original YOLOv7. In summary, the proposed method offers a better alternative or complementary approach to ship detection in SAR images than the benchmark YOLOv7 model. © 2024 IEEE.

关键词： Synthetic aperture radar

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Technological Innovations in Autonomous Vehicles: A Focus on sensor Fusion and Environmental Perception 7

Technological Innovations in Autonomous Vehicles: A Focus on...

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7th International conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Wahed, Mutaz Abdel Alzboon, Mowafaq Salem Alqaraleh, Muhyeeddin Halasa, Azmi Al-Batah, Mohammad Bader, Ahmad Fuad Faculty of Information Technology Jadara University Irbid Jordan Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (纸本)9798331519513

The automotive industry is undergoing an extensive transformation, with major car producers like Tesla, Waymo, along IT and telecom developers, advancing closer to the introduction of absolutely Autonomous Vehicles (AVs). While AVs are expected to become a mass phenomenon within the future, the adventure to fully independent using is fraught with numerous demanding situations. This paper provides an updated contrast of the technologies utilized in AVs, focusing at the performance of various sensors such as Lidar, radar, cameras, and ultrasonic sensors. It analyzes the impact of sensor fusion on environmental perception, that is crucial for the successful implementation of AVs. Using literary analysis, the study compares the differing types and volumes of information generated via these sensors, highlighting the drastic differences that stand up while extra sensors are used. The records from a couple of sensors have to be manipulated and processed via numerous ranges to be effective. The paper offers new fusion techniques that cope with contemporary challenges in autonomous riding belief duties and gives insights into future fusion-based totally techniques, which can be crucial for the big adoption of AVs. © 2024 IEEE.

关键词： Automotive industry

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Semi-Supervised Attention-Augmented Convolutional Autoencoder for radar-Based Human Activity Recognition 26

Semi-Supervised Attention-Augmented Convolutional Autoencode...

引用

conference on radar sensor technology XXVI Part of SPIE Defense and Commercial Sensing conference

作者： Campbell, Christopher Ahmad, Fauzia Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA

ISBN: (纸本)9781510650930;9781510650923

In this paper, we consider semi-supervised training of an attention-augmented convolutional autoencoder (AACAE) for human activity recognition using radar micro-Doppler signatures. The AA-CAE learns global information in addition to spatially localized features, thus enabling the classifier to overcome the limited receptive field of a conventional convolutional autoencoder (CAE). The design also permits the possibility of semi-supervised training of the AA-CAE using training data comprising unlabeled and labeled sets. More specifically, the semisupervised training regime is implemented by first pre-training the AA-CAE via unsupervised training of the attention-augmented autoencoder with the unlabeled portion of the training data. This is followed by fine-tuning of the AA-CAE for classification using the labeled portion. Using real-data measurements of six different human activities, we demonstrate that the semi-supervised AA-CAE yields higher classification accuracy with much less labeled data than a fully-supervised conventional CAE.

关键词： Machine learning latent-variable models semi-supervised learning Human activity recognition micro-Doppler unsupervised learning

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