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

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Heterogeneous data Fusion Method in Voxel-Based Scenes 11

Heterogeneous Data Fusion Method in Voxel-Based Scenes

引用

11th International conference on Information Systems and Computing technology, ISCTech 2023

作者： Xu, Qin Zhang, Lei Shen, Guochen Liu, Yixiao Tongji University Department of Traffic Information and Control Engineering Shanghai China City University of Macau Institution of Data Science China

ISBN: (纸本)9798350342406

Autonomous driving is becoming the most important implementation scenario of deep learning and a key technology affecting the industry. sensors are the key to perception of the external world in autonomous driving systems, and their collaborative performance directly determines the availability of autonomous vehicles. This paper mainly discusses the different strategies of occupancy network vision schemes and multi-sensor fusion in autonomous driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including camera, lidar, millimeter wave radar and GPS. According to recent studies, fusion strategies are divided into three categories, and sensor fusion is mainly applied to environment reconstruction. This paper discusses methods for modeling and data association for multi-objective scenarios. Finally, this paper hopes to analyze the current situation of vision scheme and multi-sensor fusion in autonomous driving, and discuss more effective and reliable fusion strategies. © 2023 IEEE.

关键词： 3D modeling

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Contactless In-Bed Movement in Various Scales Classification with CW radar 20

Contactless In-Bed Movement in Various Scales Classification...

引用

20th European radar conference, EuRAD 2023

作者： Lu, Hui Wenzel, Marvin Steigleder, Tobias Klinger, Isabell Ostgathe, Christoph Koelpin, Alexander Brandenburg University of Technology Electronics and Sensor Systems Germany Hamburg University of Technology Institute of High-Frequency Technology Germany Universitätsklinikum Erlangen Department of Palliative Medicine Germany

ISBN: (纸本)9782874870743

Continuous wave (CW) radar has been used to detect motions in various scenarios. In this paper, we first present a data-driven method to classify in-bed movement from various scales with CW radar. data augmentation techniques are used to address the small sample size problem, resulting in a significant improvement of over 10% in accuracy. Three machine learning classifiers, namely random forest, k-nearest neighbor (k-NN), and multilayer perceptron (MLP), are evaluated, with random forest demonstrating the highest accuracy of 81.94% and relative improvement of 22.5% compared to k-NN. The movement sitting up from the bed can be classified with 97.5% accuracy. Additionally, the method can classify two types of movements involving only arm and leg movements, which are not visible to the radar, by detecting small-scale joint movements from the back with an accuracy of 74.8%. © 2023 European Microwave Association (EuMA).

关键词： Machine learning

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Continuous Human Activity Classification with radar Point Clouds and Point Transformer Networks 20

Continuous Human Activity Classification with Radar Point Cl...

引用

20th European radar conference, EuRAD 2023

作者： Kruse, Nicolas C. Fioranelli, Francesco Yarovoy, Alexander Delft University of Technology MS3 Group Netherlands

ISBN: (纸本)9782874870743

Due to numerous benefits, radar is considered as an important sensor for human activity classification. The problem of classifying continuous sequences of activities of unconstrained duration has been studied in this work. To tackle this challenge, a radar data processing method utilizing point transformer networks has been proposed. The method has been experimentally verified on a dataset of human activities, and experiments have been performed to determine its optimal implementation. Promising preliminary results on a 9-class dataset show test accuracy and macro F-1 scores in the range of 83% and 73% respectively. © 2023 European Microwave Association (EuMA).

关键词： Machine learning

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Analysis and Validation of 77 GHz FMCW-based GB-SAR Micro-deformation Monitoring Method 36

Analysis and Validation of 77 GHz FMCW-based GB-SAR Micro-de...

引用

36th Chinese Control and Decision conference (CCDC)

作者： Zhou, Mengyuan Li, Chuandong Shi, Yawei Li, Huaqing Wang, Huiwei Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Since the development of deformation monitoring technology, various synthetic aperture radar (SAR) systems have been developed. There are two popular traditional deformation monitoring technologies including spaceborne SAR and airborne SAR. However, they both have the disadvantages such as the inability to monitor promptly, long monitoring cycles, and the micro-deformation accuracy of low. The ground-based synthetic aperture radar (GB-SAR) avoids these problems, which offers great convenience and potential for micro-deformation monitoring applications. This paper proposes a technique based on the 77 GHz frequency modulated continuous wave (FMCW) micro deformation monitoring method for GB-SAR, which leads to a lighter, faster GB-SAR device with better monitoring accuracy. Firstly, a fusion filtering method employing wiener filtering and 2D discrete wavelet transform (2DWT) is used to remove noise in the radar echo data. Then, a GB-SAR imaging system is developed based on a back-projection (BP) algorithm, where the radar sensor scans the target scene by moving a horizontal trajectory. A modified mean filtering approach is performed for phase denoising of interferometric image. Moreover, a discrete cosine transform-based least squares (DCT-LS) method is utilized for phase unwrapping. Finally, several experiments are conducted to demonstrate effectiveness and practicability of the proposed method, and the accuracy of deformation monitoring can reach the sub-millimeter precision (1mm).

关键词： Micro-deformation monitoring frequency modulated continuous wave (FMCW) ground-based synthetic aperture radar (GB-SAR) 2D discrete wavelet transform (2DWT) discrete cosine transform-based least squares (DCT-LS)

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MWRC3D: 3D Object Detection with Millimeter-Wave radar and Camera Fusion 7

MWRC3D: 3D Object Detection with Millimeter-Wave Radar and C...

引用

7th International conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Wang, Ren Lu, Ningyun College Of Automation Engineerin Nanjing University Of Aeronautics And Astronautics Nanjing China Lianyungang Jari Electronics Co. Ltd Smart City Division Lianyungang China

ISBN: (纸本)9798350361445

sensor fusion has become an active research field due to its numerous advantages, such as improved perception capabilities, enhanced environment understanding, and better object detection and tracking performance. However, existing multi-sensors fusion models primarily concentrate on the combination of lidar and camera and neglect the millimeter-wave radar (MWR), an inexpensive and promising sensor. To address this limitation, this paper integrates camera and MWR, and proposes a novel framework called MWRC3D to achieve low cost and efficient 3d object detection. Specifically, we first propose an attention-based deep layer aggregation (ADLA) module for learning global associations and dependencies between individual pixels in an image, which improves the ability to characterize image features. Then, we introduce deformable convolutional networks (DCNs) to model geometric transformations, and a MWR data enhancement module is employed to correct 3D offset of radar point cloud from image center point. Finally, we stitch and fuse the image features with the radar feature maps as input to the quadratic regression head to obtain accurate 3D object detection boxes. To evaluate the effectiveness of the proposed model, extensive experiments are conducted based on the nuScenes dataset. The results demonstrate that the demonstrate that our method outperforms all baselines while maintaining high efficiency. © 2024 IEEE.

关键词： Object detection

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Development of A Wireless sensor Node for Storage Bin Volume Measurement 15

Development of A Wireless Sensor Node for Storage Bin Volume...

引用

15th IEEE International conference on Humanoid, Nanotechnology, Information technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Omar, Jaafar J. Aleluya, Earl Ryan M. Clar, Steve E. Pao, Jeanette C. Salaan, Carl John O. Bahinting, Maria Fe P. Miano, Joel I. College of Computer Studies Mindanao State University-Iligan Institute of Technology Department of Computer Applications Iligan City9200 Philippines College of Engineering Mindanao State University-Iligan Institute of Technology Department of Computer Engineering and Mechatronics Iligan City9200 Philippines College of Engineering Mindanao State University-Iligan Institute of Technology Department of Electrical Engineering Iligan City9200 Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

One of the crucial processes in the food manufacturing industry is monitoring and measuring the raw products stored in large storage structures, typically called Silos. These procedures are pivotal in dictating the industry's production, quality control, and efficiency. In this study, a wireless sensor node was developed to automatically measure the volume of the product stored in the created mock-up storage bin and a 3D surface reconstruction for visualization. The sensor node utilized a single-broad computer for its computing and wireless functionality. The sensing device comprised LiDAR sensor and a servo motor for data acquisition. The system used an open-source ROS framework to simplify the software along with its data processing pipline and hardware interface integration. The single- board and the end user devices were connected to the same network and used Remote Desktop Protocol (RDP) for remote functionality. Results from three different testing setups revealed an average error of 1.309% between the actual and predicted volume of the flour materials within the mock-up storage bin. This deviation from the actual content volume implies that the developed system offered an alternative method for efficient volume measuring of the storage bin through remote data collection and 3D visualization. © 2023 IEEE.

关键词： Volume measurement

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Development of Ultrasonic radar System for Object Detection Using PIC 16F877A

Development of Ultrasonic Radar System for Object Detection ...

引用

Innovative Research in Applied Science, Engineering and technology (IRASET),conference on

作者： Abdenour Hellas Fouad Slaoui Hasnaoui Department of Engineering University of Quebec in Abitibi Témiscamingue (UQAT) Canada

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Ultrasonic radar represents a promising technology, offering reduced cost while combining efficiency and versatility. This project details the design and implementation of a radar system based on the PIC 16F877A microcontroller, capable of measuring both the distance and direction of objects in their environment. To achieve this, an ultrasonic sensor is used in combination with a stepper motor, enabling the system to scan a full 360° area. The collected data is then displayed in real-time on an LCD screen, providing clear and precise visualization of the information. The system also integrates LEDs that serve as visual indicators to signal the proximity of detected objects. When objects are at critical distances, a buzzer is activated to alert the user, adding an auditory dimension to the detection. This feature is particularly useful for applications requiring rapid response, such as surveillance, automotive, or navigation. Comprehensive tests were conducted to evaluate the system's performance. These tests included both stationary and moving targets, allowing for the verification of reliability and measurement accuracy under various conditions. The results showed that the distances measured by the system had minimal deviations from the actual distances, confirming the high precision of the device.

关键词： visualization Ultrasonic variables measurement radar measurements Microcontrollers radar detection radar radar tracking Motors Acoustics Liquid crystal displays

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Detection and tracking of non-cooperative flying obstacles using low SWaP radar and optical sensors: an experimental analysis

Detection and tracking of non-cooperative flying obstacles u...

引用

International conference on Unmanned Aircraft Systems (ICUAS)

作者： Vitiello, Federica Causa, Flavia Opromolla, Roberto Fasano, Giancarmine Univ Naples Federico II Dept Ind Engn I-80125 Naples Italy

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405935

Improvements in low altitude, non-cooperative sense and avoid are of major interest for collision hazard mitigation within the UTM/UAM/U-Space framework. In this regard, the sensing architecture must be carefully designed so that its detection and tracking performance is suitable for timely and reliable conflict assessment, while respecting size, weight, power and costs constraints, which are particularly strict for small aerial vehicles. Within this framework, an experimental assessment of non-cooperative sensing solutions based on a lightweight radar and a visual camera, respectively, is presented in this paper. Visual detections are obtained by using a Deep Learning-based neural network, while raw detections produced by the radar are first filtered based on Doppler information to remove ground clutter, and then clustered by means of a centroiding approach. The resulting detection sets are used to generate tentative and firm tracks using customized Kalman filtering techniques. Following a research plan that foresees data gathering with incremental complexity, ground-to-air tests have been carried out using a small UAV as flying intruder, and Carrier-Phase Differential GNSS to get a reference solution and assess visual-based and radar-based detection and tracking performance. Results achieved by standalone radar and visual sensing solutions clearly highlight the potential of sensor fusion strategies to take advantage of their complementary characteristics.

关键词： visualization Target tracking Laser radar radar detection radar sensor fusion radar tracking

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ST-ConvLSGRU: A radar Echo Extrapolation Method Based on LSTM and GRU 25

ST-ConvLSGRU: A Radar Echo Extrapolation Method Based on LST...

引用

25th IEEE International conferences on High Performance Computing and Communications, 9th International conference on data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Cheng, Hailang Cui, Mengmeng Xu, Xiaolong School of Computer Science and Software Nanjing University of Information Science and Technology Nanjing China Nanjing China

ISBN: (纸本)9798350330014

Severe convective weather poses a great threat to human production activities, and high-precision prediction results are of great value for accurate prevention and control of natural disasters. For example, the traditional short-term and imminent precipitation prediction methods mainly focus on the task of radar echo extrapolation. At present, the deep learning method to achieve radar echo extrapolation has become a research hotspot. These methods effectively improve the quality of prediction results from the perspective of data-driven and statistical. Although deep learning-based methods show excellent performance, they often fail to consider their high spatio-temporal resolution, resulting in high computing resource consumption and fuzzy extrapolation results. radar echo extrapolation can be regarded as a spatio-temporal prediction problem. In this paper, this problem is modelled by a recurrent neural network (ST-ConvLSGRU), which uses LSTM to improve the GRU model by making gradient connections from the temporal and spatial dimensions to establish long-term dependencies. Also configure Convolutional Block Attention Modules (CBAM) and Depthwise Separable Convolution (DSC) to achieve lower model complexity when building multi-layer models. Experiment shows that ST-ConvLSGRU has better extrapolation prediction ability than other baseline methods. © 2023 IEEE.

关键词： Extrapolation

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Building Robust Information Systems For Remote Sensing data Management 64

Building Robust Information Systems For Remote Sensing Data ...

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2023 IEEE 64th International Scientific conference on Information technology and Management Science of Riga Technical University, ITMS 2023

作者： Osipovs, P.A. Fraimans, M. Bliznuks, D. Kovalovs, M. Riga Technical University Institute of Smart Computer Technologies Riga Latvia

ISBN: (纸本)9798350370294

The article describes the experience of building an information system for collecting and processing data from remote sensors. The specificity of the organization of a local system that provides storage, visualization [1] and processing of data from a certain set of external sensors is considered. Both the general architecture of the system and its modules are described, which provide a local graphical user interface, asynchronous data collection, visualization, notifications about reaching unacceptable values by the sensor, and processing of sensor data. The structure of interaction of the system with an external WEB-site through the implementation of the REST API [2] is shown. The issues of software emulation of sensors and risk management were also touched upon. © 2023 IEEE.

关键词： Python

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