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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 Signal-based Multi-level Association strategy for Real-time Track Association and Fusion 9

Multi Sensor Signal-based Multi-level Association strategy f...

引用

9th International conference on Big data and Information Analytics, BigDIA 2023

作者： Guo, Jiaxin Fang, Zhi Yu, Zengwen Zhang, Hang Zheng, Yifan Zhang, Hong Beijing Institute of Computer Technology and Application Beijing China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9798350330076

The real-time track association and fusion task(the AF task) aims to integrate the tracks of maritime targets collected by sensors into a coherent track. In real-world scenarios, track data collected by a single sensor often lacks the required stability, falling short of meeting the demands of various applications. While adopting multi-sensor-based AF task methods is prevalent, existing approaches have overlooked critical aspects, including temporal consistency. This paper tackles crucial challenges in the multi-sensor AF tasks, focusing on problems such as asynchronous radar scanning and real-time matching across multiple sensors. Utilizing multi-level association and integrated learning algorithms, we enhance matching accuracy and efficiency while ensuring track continuity. Leveraging the dataset provided by the 2023 National Big data and Computational Intelligence Challenge 1, our approach significantly outperforms traditional methods in three key aspects: the AF task of radar point trace , the AF task of ESM bearing line, and the AF task of rader and ESM. In comparison with conventional methods, three values are reduced by 1.1325, 0.0870, and 0.2637 on the difference metrics between the generated tracks and the actual tracks, respectively. These results further validate the significant improvements of our method in enhancing the effectiveness of the AF task. © 2023 IEEE.

关键词： multiple sensors Real-time track association and fusion

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IoT Based radar System using Ultrasonic sensor for Enhanced Object Detection and Tracking

IoT Based Radar System using Ultrasonic Sensor for Enhanced ...

引用

2023 IEEE Global conference on Information Technologies and Communications, GCITC 2023

作者： Jadhav, Balasaheb Wanjale, Pooja Gavli, Renuka Thopate, Kaushalya Maharnawar, Niranjan Karmankar, Nikhil Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Technology Department of Engineering Sciences and Humanities Pune India Ajeenkya D. Y. Patil School of Engineering Department-Computer Engineering India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Sagitech Solutions Pvt. Ltd. India

ISBN: (纸本)9798350308167

a radar system built on the Internet of Things (IoT) that uses ultrasonic sensors to find things within 150 degrees. The system's rotation is controlled by a servo motor, and a Graphical User Interface (GUI) built using Java Swing and applets displays the results of detection. In addition, the system has a feature that, upon the observation of an object, sends notifications to a user's mobile device. The proposed system can be used to detect objects in its vicinity efficiently and effectively in a variety of scenarios, such as security surveillance and environmental monitoring. The system's hardware and software components are covered in the article along with the testing and validation processes applied to gauge the system's performance. The suggested IoT-based radar system can, in general, improve object recognition abilities and give users real-time notifications, making it a desirable complement to the current range of IoT devices. © 2023 IEEE.

关键词： Arduino IoT object detection radar tracking system Ultrasonic sensor

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radar technical language modeling with named entity recognition and text classification 26

Radar technical language modeling with named entity recognit...

引用

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

作者： Zaunegger, Jackson S. Singerman, Paul G. Narayanan, Ram M. O'Rourke, Sean M. Rangaswamy, Muralidhar Penn State Univ University Pk PA 16802 USA Air Force Res Lab Sensors Directorate Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510650930;9781510650923

This paper introduces the radar text data set (radarTD) for technical language modeling. This data set is comprised of sentences containing radar parameters, values, and units determined from real-world values. This data set is created based on values determined from published academic research. Additionally, each statement is assigned a sentiment label and goal priority label. Preliminary investigations into the applicability of this data set are explored using the BERT model and several bi-directional LSTM models. These models are evaluated on text classification and named entity recognition tasks. This study evaluates the applicability of technical language modeling using neural networks to analyze input statements for cognitive radar applications. These findings suggest that this data set can be used to achieve reasonable performance for both text classification and named entity recognition for autonomous radar applications.

关键词： Text Classification Named Entity Recognition Cognitive radar Language Modeling Neural Networks

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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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Multi-Target Pedestrian Tracking Using radar and Visual Detection Information Fusion 8

Multi-Target Pedestrian Tracking Using Radar and Visual Dete...

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8th International conference on Power and Renewable Energy, ICPRE 2023

作者： Lin, Yuan Zhou, Wenju Wang, Shunan Ruan, Wei Shanghai University School of Mechatronic Engineering and Automation Shanghai China Shanghai Anda Electronics Technology Co. Ltd. Shanghai China

ISBN: (纸本)9798350328813

Pedestrian Multi-object Tracking (MOT) is crucial in surveillance, autonomous driving, and pedestrian density detection. An important challenge in MOT is detecting environmental disturbances like bad weather, occlusion, or tracking failures due to distance. Current methods rely on machine vision, extracting features and establishing trackers. However, these methods solely depend on image data, limiting detection accuracy in unstable conditions. To address this, a method is proposed that combines millimeter wave radar and camera sensors. The radar provides anti-interference, range, and depth measurements, while the camera offers target recognition and orientation. In dealing with the detection relationship between the two sensors, the distance measure in the logarithmic domain is used for matching. In terms of detection results and tracking, the utilization rate of detection results is improved by using the method of secondary matching. This fusion method reduces computational burden and improves stability by complementing missing data from any sensor. The MMW radar and camera fusion tracking algorithm is validated using an experimental platform, demonstrating significant performance enhancements compared to single-sensor tracking. © 2023 IEEE.

关键词： Camera and radar data fusion Kalman filter Pedestrian tracking

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AI-based Human Detection and Localization in Heavy Smoke using radar and IR Camera

AI-based Human Detection and Localization in Heavy Smoke usi...

引用

IEEE radar conference (radarConf)

作者： Kulhandjian, Hovannes Davis, Alexander Leong, Lancelot Bendot, Michael Kulhandjian, Michel Calif State Univ Fresno Dept Elect & Comp Engn Fresno CA 93740 USA Rice Univ Dept Elect & Comp Engn Houston TX USA

ISBN: (纸本)9781665436694

One of the main challenges currently firefighters are facing in search and rescue operations is battling the heavy smoke inside a space that needs to be searched for people and animals. In this work, we develop an integrated system composed of two unique sensing mechanisms that are capable of real-time detection and localization of humans and animals in deep smoke to improve the situational awareness of firefighters on the scene. We make use of data from a micro-Doppler sensor and an infrared camera and train a DCNN algorithm to localize a human in dense smoke in real-time. Experimental results reveal that the proposed system can detect a human in heavy smoke with an average of 98% validation accuracy.

关键词： Deep learning human detection in heavy smoke data fusion artificial intelligence

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RESEARCH PROGRESS ON WIRELESS sensor NETWORKS FOR MECHANICAL VIBRATION MONITORING: A CITESPACE visualization ANALYSIS APPROACH 10

RESEARCH PROGRESS ON WIRELESS SENSOR NETWORKS FOR MECHANICAL...

引用

10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Zha, Zhendong Jiang, Zhanglei Li, Zihan Liu, Xiuli Wu, Guoxin The Ministry of Education Key Laboratory of Modern Measurement and Control Technology Beijing Information Science & Technology University Beijing100192 China

ISBN: (纸本)9781837242672

The application of wireless sensor networks (WSNs) in mechanical vibration monitoring is pivotal for achieving efficient monitoring within the realms of industrial automation and intelligent manufacturing, playing a significant role in the digital transformation and upgrading of China's manufacturing industry. This paper provides a comprehensive overview of the major technological advancements made by scholars and institutions globally over the past sixteen years in the domain of mechanical vibration monitoring using WSNs. Subsequently, employing CiteSpace for knowledge mapping and visualization, a multifaceted statistical analysis is conducted on over four hundred publications from both domestic and international sources. Through clustering analysis, keyword co-occurrence, and other analytical techniques, the paper delineates the multidimensional evolution of research hotspots and the dynamics of author collaboration networks over time. Building on this foundation, the paper delves into the cutting-edge trends of the field, emphasizing the critical importance of establishing efficient energy management systems and leveraging machine learning and artificial intelligence to process large-scale sensor data. These strategies are essential for optimizing system performance and propelling the development of WSNs towards miniaturization, integration, multifunctionality, systematization, and networking. The synergistic effects of these measures are anticipated to provide robust support for the intelligent upgrade of the manufacturing industry, fostering its progression towards greater efficiency and intelligence. © The Institution of Engineering & technology 2024.

关键词： Information management

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radar and Camera Fusion Dehazing for Road Vehicle Detection in Haze Weather 26

Radar and Camera Fusion Dehazing for Road Vehicle Detection ...

引用

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

作者： Bi, Xin Weng, Caien Zhang, Bo Liu, Yong Tong, Panpan Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

ISBN: (纸本)9798350399462

Object detection is a necessary part of the intelligent driving system, especially in some severe weather conditions like haze. However, in haze weather, images acquired by the camera are degraded, which severely impacts the subsequent detection process. The radar performance is almost unaffected by haze. Based on the above background, we focus on the multi-sensor fusion dehazing algorithm applied to vehicle detection in hazy road environments, using radar data to provide information supplements for dehazing. First, we propose a radar-camera fusion dehazing algorithm based on the atmospheric scattering model. The radar detection information is used to provide direct and accurate transmission estimation and haze removal region of interest for the dehazing algorithm, so as to achieve precise dehazing effects focusing on dynamic vehicle targets on the road. Second, we apply YOLOv5 to haze-removal images. The proposed fusion dehazing algorithm is tested on the synthetic haze road environment and the real-world dataset, respectively. The average precision of the object detection task is used as the evaluation metric. Finally, experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art dehazing algorithms.

关键词： Object detection

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Microwave Photonics Computational Signal Algebra Framework for radar Integrated Sensing and Communication Applications 5

Microwave Photonics Computational Signal Algebra Framework f...

引用

5th IEEE MTT-S Latin America Microwave conference, LAMC 2025

作者： Aceros, Cesar Valera-Marquez, Juan Aviles, Yvonne Rodriguez, Domingo UTS Santander Colombia Universidad de Puerto Rico Río Piedras Puerto Rico Universidad de Puerto Rico Mayagüez Puerto Rico

ISBN: (纸本)9798331540401

This work presents a computational signal algebra (CSA) framework for microwave photonics signal processing efforts in radar integrated sensing and communication (RISC) applications, for areas such as parameter estimation, beamforming, and waveform design. The proposed framework presents a set of computational methods, with an associated visualization environment for time-frequency tools such as the short-time Fourier transform (STFT), the cross-ambiguity function (CAF), and the Wigner-Ville distribution (WVD). The discrete ambiguity function (DAF) was implemented as a user defined function (UDF) to be used as a signal correlation analysis and waveform design tool aiming at assisting in research efforts towards efficient use of the electromagnetic spectrum by reducing noise and interference and adhering to spectrum management regulatory standards. Significant computational speedups were obtained for the CAF by introducing novel data sampling algorithmic techniques. Finally, an information flow security framework is proposed utilizing a post-quantum cryptography (PQC) approach in RISC environments. © 2025 IEEE.

关键词： Wigner-Ville distribution

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A Compact Fractal Microstrip Patch Antenna for Skin Cancer Detection Using Monostatic radar-Based Microwave Imaging Technique

A Compact Fractal Microstrip Patch Antenna for Skin Cancer D...

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2024 IEEE Microwaves, Antennas, and Propagation conference, MAPCON 2024

作者： Kaur, Komalpreet Kaur, Amanpreet Department of Electronics and Communication Thapar Institute of Engineering and Technology Punjab Patiala India

ISBN: (纸本)9798350379693

The inherent advantages of being non-invasive and non-ionizing have drawn the attention of cancer researchers to microwave imaging (MWI). Therefore, this study focuses on monostatic radar-based microwave imaging (MRMWI) of skin cancer using a compact Ultra-Wide Band (UWB) Hybrid Fractal Microstrip Patch Antenna (HFMPA). The HFMPA functions as a sensor and has dimensions of 24×20×0.8 mm3. It comprises a combination of Pythagoras tree and Snowflake-shaped resonators alongside a defective ground surface (DGS) and a slotted microstrip feed line. It operates within the 5.71-16.94 GHz frequency range with a peak gain of 5.1 dB at 16 GHz. The performance analysis of the proposed HFMPA as a sensor for MRMWI has been carried out in the vicinity of a four-layered design bio-mimic of the forearm in Computer Simulation technology (CST) Microwave Studio (MWS) software. The HFMPA is positioned 19 mm from the bio-mimic of the forearm to collect backscattered signals (S11) at different positions around it. The collected data is pre-processed and then used with the Coherent-Factor Delay Multiply and Sum (CF-DMAS) beamforming algorithm to generate images of the inspected regions. The antenna's specific absorption rate (SAR) is 1.38 W/Kg for 1 g of tissue, indicating safe for human exposure. © 2024 IEEE.

关键词： Microstrip antennas

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