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

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

Multi-Sensor Fusion Localization Algorithms for Intelligent ...

引用

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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Development of a Flexible and User-Friendly UI to Visualize the Invisible Pressure Distribution

Development of a Flexible and User-Friendly UI to Visualize ...

引用

Gaming, Entertainment and Media conference (GEM)

作者： Minakawa, Keigo Sato, Kazuma Jing, Lei Univ Aizu Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9798350374537;9798350374544

In this study, we propose a method to facilitate the observation and analysis of pressure data. In modern times, research utilizing pressure is advancing in various fields. However, the aspect of visualizing invisible pressure is often overlooked and not well-developed. Therefore, we have developed a pressure distribution visualization system that allows users to easily acquire and analyze pressure data transmitted from pressure sensors through a user interface (UI). The application is not limited to just one type of pressure sensor device with specific specifications. it needs to meet various requirements. The system is developed as a mobile application for mobile devices, capable of displaying pressure in both grid and hand-shaped modes. Furthermore, its versatility allows it to accommodate various requirements of pressure sensors. In the usability evaluation experiment conducted on the application developed in this study, an impressive score of 83.5 was achieved, indicating a high level of usability.

关键词： visualization UI Pressure sensor Mobile application

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A Study on Point Cloud Clustering for Geo-referenced Positioning using mmWave radar sensor 29

A Study on Point Cloud Clustering for Geo-referenced Positio...

引用

29th IEEE Asia Pacific conference on Communications, APCC 2024

作者： Park, Daeseung Choi, Jaemin Chang, Hojong Lee, Chae-Seok Kaist Convergence Research Center for College of Engineering Daejeon Korea Republic of

ISBN: (纸本)9798331517816

In the field of urban safety, extensive research is being conducted on the application of mmWave radar sensors for both indoor and outdoor environments. These advanced sensing technologies are gaining attention due to their potential to enhance monitoring and response capabilities in complex urban settings. Traditional CCTV-based geo-referenced positioning technology predicts Geographic coordinates data to enable real-time site response in the event of an incident. However, pixel-based relative geo-referenced methods of CCTV suffer from low recognition rates and precision due to terrain characteristics. mmWave radar sensors, in contrast, are minimally affected by weather conditions, use high frequencies in the 60-63 GHz range, and offer high precision. Additionally, these sensors can penetrate structures up to 15 cm thick. Despite these advantages, the number, shape, and position of recognized objects in three-dimensional space vary depending on the sensor installation angle and position, complicating the application of geo-referenced technology for accurate positioning. Consequently, mmWave radar sensors have not been widely utilized for positioning. This study proposed and implemented a geo-referenced positioning model using mmWave radar sensors to address these technical challenges. The proposed model employed a dimension transformation clustering technique based on orthogonal projection methods. Implementation and experimental results demonstrated that the average georeferenced error using mmWave radar sensors was reduced to 3.5 cm, representing a 95.33 % improvement in precision compared to traditional CCTV-based geo-referenced methods. © 2024 IEEE.

关键词： Geo-referenced positioning Improving performance mmWave radar sensor

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Harnessing Machine Learning based sequence prediction for Industry 4.0 applications 15

Harnessing Machine Learning based sequence prediction for In...

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kanagachidambaresan, G.R. Arun, C.A. Subashini, M. Maheswaran, S. VelTech Rangarajan Dr.Sagunthala R&d institute of Science and Technology Dept of Cse Chennai India VelTech Rangarajan Dr.Sagunthala R&d institute of Science and Technology Dept of Ece Chennai India VelTec Rangarajan Dr.Sagunthala R&d institute of Science and Technology Jrf IoT Experts System Laboratory Chennai India Kongu Engineering College Department of Electronics and Communication Engineering Perundurai India

ISBN: (纸本)9798350370249

This paper focuses on the utilization of gyro sensor data, particularly from the MPU6050 sensor, for sequence prediction and positional analysis for many Industry 4.0 application and asset monitoring. Leveraging machine learning algorithms such as Random Forest, Decision Trees etc., a robust classification model is developed to predict sequences of positions with notable accuracy. data collection involves capturing gyro sensor readings across distinct positions, laying the foundation for subsequent analysis. Integration with the Thingsboard platform facilitates data processing and analysis, showcasing the effectiveness of this integrated approach. Additionally, a user-friendly interface is designed for streamlined data input and visualization of prediction outcomes, enhancing accessibility and usability. The model demonstrates high accuracy in predicting sequences of positions using Random Forest and Decision Trees algorithms. The Random Forest and Decision tree approach provides higher accuracy when compared with the other algorithms. A IoT based hardware is designed in Kicad and tested to validate the ML based prediction for shop floor applications. © 2024 IEEE.

关键词： data visualization Machine Learning MPU6050 sensor Positional Analysis Thingsboard Platform User Interface

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Non-contact sensor-based human fall detection method 3

Non-contact sensor-based human fall detection method

引用

3rd International conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Ruan, Tingting Yan, Tianxing Song, Yongkun Zhang, Ke Liu, Xian Changsha Health Vlocational College ChangSha41000 China Changsha University of Science & Technology Changsha410000 China

ISBN: (纸本)9781510681279

With the exacerbation of global population aging, the issue of falls among the elderly is increasingly drawing attention from various sectors of society. Consequently, the development of an effective human fall detection system is crucial for timely identification of fall incidents and provision of emergency assistance. This paper proposes a fall detection method based on non-contact sensors, aiming to achieve real-time monitoring and accurate identification of fall events through advanced sensor technology and data processing algorithms. Initially, this study employs low-frequency ultra-wideband radar (UWB) as the primary tool for data acquisition, followed by the utilization of deep learning techniques to extract key dynamic features from radar data, thus providing a reliable foundation for fall event identification. Furthermore, this paper designs and implements a fall recognition model based on Convolutional Neural Networks (CNN), which, through training on a large amount of radar image data, learns to distinguish patterns between normal activities and falling behavior. The results demonstrate that the proposed method can effectively detect falls under various common daily activities, with a recognition rate as high as 99.338%. © 2024 SPIE.

关键词： data acquisition

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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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radar Target Detection Method through Spatial Voxelization in Complex Indoor Environment

Radar Target Detection Method through Spatial Voxelization i...

引用

2023 IEEE International conference on Consumer Electronics-Asia, ICCE-Asia 2023

作者： Lee, Jaewon Jang, Dalwon Lee, JongSeol Information Media Research Center Korea Republic of

ISBN: (纸本)9798350344318

This paper is about a method for extracting the target point of a signal received from a radar sensor through spatial voxelization in an indoor environment with many metal objects such as partitions, PCs, and monitors. radar sensors are widely used in autonomous driving, sensor data convergence, etc., and various methods have been studied to find the reference point of the radar sensor. The conventional method was to measure a signal in a calibration room without any objects or to find a reference point using a plurality of corner reflectors. However, we propose a method of voxelizing a space with a single corner reflector in a complex environment and extracting an accurate target point with only a specific range of RCS(radar Cross Section) values. The validity of the proposed algorithm was verified through experiments and analysis in various locations, and it was confirmed that it was effective. © 2023 IEEE.

关键词： radar cross section

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RESEARCH ON MULTI-sensor FUSION technology OF PERCEPTUAL sensorS IN DRIVERLESS VEHICLES 2

RESEARCH ON MULTI-SENSOR FUSION TECHNOLOGY OF PERCEPTUAL SEN...

引用

2nd International conference on Mechatronic Automation and Electrical Engineering, ICMAEE 2024

作者： Xie, Yinxin Shandong Agricultural University Taian271018 China

ISBN: (纸本)9781837242672

Self-driving vehicles need to detect the environment, systematically process the input data make decisions, and assign tasks to the executing agency until the task is executed. The environmental data processed by the system is obtained by perceptual sensors, which allow self-driving vehicles to judge their environment and drive reasonably. However, these perception sensors have space-time limitations, and different perception sensors also have different advantages and disadvantages, limited by weather, climate, light, item size, and other factors. Therefore, to further improve the reliability and stability of self-driving vehicles, so that self-driving vehicles finally reach the L5 level, the multi-sensor fusion technology of perceptual sensors is of great significance. The research in this paper focuses on various types of perception sensors and gives full play to their advantages through sensor fusion technology. The perception sensor fusion technologies studied in this article, namely camera-millimetre-wave radar fusion and camera-lidar fusion, can solve the data space-time errors between unfused sensors, and can also give full play to their advantages to make data more accurately identified. In the field of autonomous driving, it helps computer systems to complete the calculation goals more accurately and quickly and improves the stability of selfdriving vehicles. © The Institution of Engineering & technology 2024.

关键词： sensor data fusion

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Multi-Band Hybrid Active-Passive radar sensor Fusion

Multi-Band Hybrid Active-Passive Radar Sensor Fusion

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IEEE radar conference (radarConf)

作者： Beasley, Piers J. Ritchie, Matthew A. UCL Dept Elect & Elect Engn London England

ISBN: (纸本)9781665436694

In this paper the topic of joint active and passive (hybrid) radar detection is introduced and the theoretical benefits are outlined. An experimental hybrid radar setup is presented where a low-cost Software Defined Radio (SDR) based radar system is used for hybrid sensing of targets using active and Passive Bistatic radar (PBR). Experimental results are presented for simultaneously sensing using an active 2.4 GHz radar and 690 MHz Digital Video Broadcasting - Terrestrial (DVB-T) based PBR mode. The detection performance of each sensor and a joint sensor performance are evaluated, where the joint detection performance is found to exceed that of the individual sensors alone. The ability to reduce active radar transmissions, but still retain a reasonable detection performance, is investigated using experimental data and the case is made for adaptive behaviour in order to exploit the benefits available to hybrid radars.

关键词： Hybrid radar Multistatic radar Passive radar Passive Bistatic radar Software Defined Radio Software Defined radar

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radar Signal Abnormal Point Classification based on Camera-radar sensor Fusion 5

Radar Signal Abnormal Point Classification based on Camera-R...

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5th International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Seo, Hyojeong Han, Dong Seog Kyungpook Natl Univ Sch Elect & Elect Engn Daegu South Korea Kyungpook Natl Univ Sch Elect Engn Daegu South Korea

ISBN: (纸本)9781665456456

For safe driving, it is essential to accept reliable information from recognition sensors. In this paper, we present a deep learning model that classifies whether radar signals coming in are normal or abnormal. The abnormal signal is defined as noise from the radar and all signals received when the radar fails or is in trouble. It is difficult to determine whether reflected signals are normal or not based only on radar data. Therefore, the camera and radar sensors are used together, considering the radar cross section (RCS) distribution varies by the angle and distance of the object. The proposed model uses data received from camera and radar sensors to determine the normality of object signals. The model shows an accuracy of 96.24%. Through the results of this study, the reliability of radar signals can be determined in the actual driving environment, thereby ensuring the safety of vehicles and pedestrians.

关键词： radar RCS deep learning classification sensor fusion

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