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16th International conference on Signal Processing Systems, ICSPS 2024

作者： Wang, Wanbin Zou, Lin School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510689251

The Millimeter-wave radar has become an important sensor for advanced driver assistance systems due to its advantages such as environmental adaptability, low cost and detection accuracy. Most of current vehicle radars use Multiple Input Multiple Output (MIMO) technology to obtain better angular resolution. Among all MIMO radar waveforms, both CDMA and DDMA meet the requirements of next-generation high-performance radars and may become the mainstream radar waveforms in the future. Performance comparisons of the two waveforms are scattered in a large number of literatures and they are all from a theoretical point of view. At present, there is no literature comparing the practical performance of CDMA and DDMA. AWR6843 is a high-precision millimeter-wave radar sensor widely used in automotive safety, environmental monitoring and other applications. This SOC chip has been adopted in numerous automotive radar products. Thus, based on the data collected by a TI's AWR6843 radar evaluation board, this paper compares the performance of CDMA and DDMA in terms of waveform orthogonality, parameter estimation capability, and processing complexity. In addition, the paper discusses the merits and demerits of each technology and the limitations of its practical application, which provides an important reference for the optimization and technical route selection of future radar systems. © 2025 SPIE.

关键词： Advanced driver assistance systems

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Gait Pattern Analysis of Older Adults via radar Micro-Doppler Signatures and Wrist-worn Accelerometer data 28

Gait Pattern Analysis of Older Adults via Radar Micro-Dopple...

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conference on radar sensor technology XXVIII

作者： Ahmad, Fauzia Adl, Zahra Sadeghi Hagen, Cole Hiremath, Shivayogi, V Uhl, Astrid Ferretti, Lisa McCallion, Philip Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA Temple Univ Dept Hlth & Rehabil Sci Philadelphia PA 19122 USA Temple Univ Sch Social Work Philadelphia PA 19122 USA

ISBN: (纸本)9781510674158;9781510674141

In this paper, we report the results of a radar and accelerometer based pilot observational study of gait patterns of elderly participants enrolled in an exercise program aimed at improving strength, balance, and agility. We employ a radar system and a wearable accelerometer device to capture biomechanical movements of the participants as they walk back and forth in front of a radar. We extract gait parameters by analyzing the Doppler signatures obtained from the radar measurements and time-series data from the accelerometer device worn on the wrist while walking. Additionally, we record physical activity levels of participants over a two-week period using the wrist-worn accelerometer device and determine duration of moderate-to-vigorous physical activity (MVPA). The gait parameters and MVPA duration, extracted from two separate sets of measurements made prior to and at the conclusion of the exercise program, are used to assess potential changes in the gait and mobility of the participants. Using percentage change in parameter values as a metric, the results generally demonstrate a positive impact of the exercise program on gait and physical activity levels. At the same time, an appreciable categorical agreement is observed between the two sensing modalities.

关键词： Micro-Doppler radar gait accelerometer

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Doppler Measurement-Based Positioning and Tracking Methods with External Radiation radar 2

Doppler Measurement-Based Positioning and Tracking Methods w...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Liu, Weifeng He, Linqing Tian, Zhengwang Shaanxi University of Science and Technology School of Electrical and Control Engineering Xi'an China Hangzhou Dianzi University School of Automation Hangzhou China

ISBN: (纸本)9798331515669

This paper introduces a new approach for addressing the complex problem of Multi-target tracking in external radiation radar systems equipped with Multi-static Doppler measurements. Drawing from research on sensor arrays and mobile sensors, the method utilizes the Generalized Labelled Multi-Bernoulli (GLMB) filter to navigate the intricacies of Multi-target tracking across sensor arrays. The study validates the effectiveness of the GLMB and the computationally efficient Gibbs-GLMB filters in a setup involving three transmitters and one receiver. Experimental results, assessed with the Optimal Sub-Pattern Assignment (OSPA) metric, suggest that the Gibbs-GLMB filter accelerates computation while potentially compromising on measurement precision, which could lead to reduced accuracy in specific cases. The investigation also examines the influence of sensor geometry on tracking results, indicating that there is an association between tracking performance and sensor arrangement. © 2024 IEEE.

关键词： radar target recognition

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Fusion of Image and Pointcloud data for 3D Object Detection with 4D Imaging radar 2

Fusion of Image and Pointcloud Data for 3D Object Detection ...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Li, Xiaozhi Pang, Zhaochen Li, Jian Ministry of Education Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing China

ISBN: (纸本)9798331515669

3D object detection aims to extract the spatial positions of objects from sensor data, thereby improving environmental awareness and comprehension. With the advancement of radar technology, the 4D imaging radar offers greater robustness in adverse weather conditions compared to LiDAR sensors, thus expanding its potential applications. However, due to the relative scarcity of 4D imaging radar datasets and the inherent sparsity of the radar point clouds, traditional point cloud-based 3D detection models perform suboptimally when applied directly. This paper focuses on the 3D object detection task with multimodal sensors, exploring the integration of RGB image features and 4D imaging radar point cloud features. We propose an innovative multi-modal detection fusion algorithm that markedly enhances detection accuracy on the VoD dataset. © 2024 IEEE.

关键词： radar imaging

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Quantifying Knowledge: Medical Applications of radar Aging through the Lens of Bibliometrics 21

Quantifying Knowledge: Medical Applications of Radar Aging t...

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conference on Smart Biomedical and Physiological sensor technology XXI

作者： Obaideen, Khaled Faroukh, Yousuf Bonny, Talal Al-Shabi, Mohammad Alobaid, Ahmad Univ Sharjah Res Inst Sci & Engn Sharjah U Arab Emirates Univ Sharjah Sharjah Acad Astron Space Sci & Technol CubeSat Lab Sharjah U Arab Emirates Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates Univ Sharjah Coll Engn Sharjah U Arab Emirates

ISBN: (纸本)9781510674370;9781510674363

This research plunges into the rapidly growing radar technology in the medical sector, putting emphasis on its possibility to revolutionize elderly care and health monitoring among the aging global population. Based on a systematic literature review and rigorous bibliometric analysis, we discuss radar technology application in healthcare, focusing on its potential for non-invasive, high-accuracy diagnosis and continuous patient monitoring. Our findings highlight the critical harmony between radar technology and the advances in machine learning, artificial intelligence, and data analytics, which open the door to smart healthcare solutions. These advancements will improve early disease detection, fall risk prevention, and real-time health monitoring, resulting in quick medical responses. This study endeavors to offer useful knowledge to researchers, practitioners, and policymakers who are working towards the use of technology for better health in the context of the demographic changes that the world is experiencing in terms of an ageing population by mapping the current research landscape, identifying the existing trends and gaps, and proposing the future direction of research.

关键词： radar Rlderly Care VOSviewer Bibliometric

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Hardware-Accelerated data Decoding and Reconstruction for Automotive LiDAR sensors

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IEEE TRANSACTIONS ON VEHICULAR technology 2023年第4期72卷 4267-4276页

作者： Cunha, Luis Roriz, Ricardo Pinto, Sandro Gomes, Tiago Univ Minho Ctr ALGORITMI LASI P-4710057 Braga Portugal

The automotive industry is facing an unprecedented technological transformation towards fully autonomous vehicles. Optimists predict that, by 2030, cars will be sufficiently reliable, affordable, and common to displace most current human driving tasks. To cope with these trends, autonomous vehicles require reliable perception systems to hear and see all the surroundings, being light detection and ranging (LiDAR) sensors a key instrument for recreating a 3D visualization of the world. However, for a reliable operation, such systems require LiDAR sensors to provide high-resolution 3D representations of the car's vicinity, which results in millions of data points to be processed in real-time. With this article we propose the ALFA-Pi, a data packet decoder and reconstruction system fully deployed on an embedded reconfigurable hardware platform. By resorting to field-programmable gate array (FPGA) technology, ALFA-Pi is able to interface different LiDAR sensors at the same time, while providing custom representation outputs to high-level perception systems. By accelerating the LiDAR interface, the proposed system outperforms current software-only approaches, achieving lower latency in the data acquisition and data decoding tasks while reaching high performance ratios.

关键词： sensors Laser radar sensor systems Decoding Point cloud compression Automotive engineering Software Autonomous vehicles LiDAR FPGA data representation LiDAR point cloud

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Extended Kalman-based multi-sensor fusion 39

Extended Kalman-based multi-sensor fusion

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39th Youth Academic Annual conference of Chinese-Association-of-Automation (YAC)

作者： Bali, Guzhalayi Xie, Leong Li, Penghua Ilolov, Mamadsho Xinjiang Univ Sch Elect Engn Urumqi Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing Peoples R China Natl Acad Sci Tajikistan Ctr Innovat Dev Sci & New Technol Dushanbe Tajikistan

ISBN: (纸本)9798350390780;9798350379228

With the continuous development of sensor technology and electronic information technology, data fusion technology is of great significance in improving the system's ability of detecting, recognizing and tracking targets. That is, a single sensor can only provide partially imprecise information, thus not excluding ambiguity in the description of the external environment. Given the complementarity exhibited by the differences in the performance of the sensors, synthesizing the information from individual sensors results in more accurate and reliable information than a single sensor can provide. A multi-sensor fusion method based on extended Kalman is proposed to address this problem. Experimental results show that the accuracy and robustness of target detection and tracking can be significantly improved by the fusion of infrared and millimeter wave radar data.

关键词： Extended Kalman filtering multi-sensor fusion temporal calibration

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Automatic Multi-sensor dataset Generation in Autonomous Vehicle Environments 23

Automatic Multi-Sensor Dataset Generation in Autonomous Vehi...

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23rd International conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Deng, Zong-Yue Kang, Li-Wei Chiang, Hsin-Han National Taiwan Normal University Department of Electrical Engineering Taipei Taiwan National Taipei University of Technology Department of Vehicle Engineering Taipei Taiwan

ISBN: (纸本)9798331528041

This paper presents a comprehensive method for dataset construction, utilizing 3D object detection to automatically label objects detected by LiDAR sensors and synchronizing multi-sensor labeling through coordinate calibration, thereby automatically generating image and radar datasets that support various learning algorithms. Initially, the cocalibration from the camera, radar, and LiDAR sensors is conducted to standardize the coordinate system based on the LiDAR. The camera output includes image information, encompassing object depth and related data. The radar sensor, particularly in automotive applications, returns data on the position of objects in front of the vehicle. Further, the Hungarian Algorithm is employed to analyze the association between radar and camera-detected objects. The proposed collaboration process with software workflow for automatic dataset generation with multi-sensors is detailed in this study. Finally, the preliminary results from sensor fusion over single-sensor modalities to object detection applications are presented to facilitate the efficient and rapid development of our approach to multi-sensor dataset generation, which is still extremely limited to the optical counterparts in autonomous vehicle environments. © 2024 IEEE.

关键词： radar imaging

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Diffusion-Based Point Cloud Super-Resolution for mmWave radar data

Diffusion-Based Point Cloud Super-Resolution for mmWave Rada...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Luan, Kai Shi, Chenghao Wang, Neng Cheng, Yuwei Lu, Huimin Chen, Xieyuanli Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China Tsinghua Univ Beijing Peoples R China ORCA TECH North Sydney NSW Australia

ISBN: (纸本)9798350384581;9798350384574

The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics. However, the radar point clouds are relatively sparse and contain massive ghost points, which greatly limits the development of mmWave radar technology. In this paper, we propose a novel point cloud super-resolution approach for 3D mmWave radar data, named radar-diffusion. Our approach employs the diffusion model defined by mean-reverting stochastic differential equations (SDE). Using our proposed new objective function with supervision from corresponding LiDAR point clouds, our approach efficiently handles radar ghost points and enhances the sparse mmWave radar point clouds to dense LiDAR-like point clouds. We evaluate our approach on two different datasets, and the experimental results show that our method outperforms the state-of-the-art baseline methods in 3D radar super-resolution tasks. Furthermore, we demonstrate that our enhanced radar point cloud is capable of downstream radar point-based registration tasks.

关键词： Stochastic systems

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Implementation of Verification Simulator for MIMO radar Front-End Module Verification

Implementation of Verification Simulator for MIMO Radar Fron...

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2024 IEEE International conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Jin, YoungSeok Bae, Jieun Park, Chi Ho Hyun, Eugin Division of Automotive Technology DGIST Daegu Korea Republic of Division of AI Big data and Block chain DGIST Daegu Korea Republic of

ISBN: (纸本)9798331530839

This study implemented a simulator for verification of a MIMO radar front-end module. The implemented simulator was validated using simulated data produced in MATLAB. The verification results confirmed that the simulator can be used to assess errors in the front-end module's receive channels, as well as in range, velocity, and angle measurements. © 2024 IEEE.

关键词： MIMO radar

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