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

共 4468 条记录，以下是31-40 订阅

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Change Detection Algorithms for Ad Hoc Distributed radar 28

Change Detection Algorithms for Ad Hoc Distributed Radar

引用

conference on radar sensor technology XXVIII

作者： Marengo, Edwin A. Vedral, James Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Kostas Res Inst Burlington MA 01803 USA

ISBN: (纸本)9781510674158;9781510674141

This work investigates the detection of targets in an unknown complex scene by means of an ad hoc network of widely distributed sensors that are spread over the region of surveillance. A general coherent change detection methodology is developed that is based on the classical optical theorem of wave scattering theory. It relies on spatial-temporal-spectral projections of the scattered field data onto the incident field or background medium response data. This involves only data gathered in situ by the sensing array. Thus the proposed approach is purely data-driven, an important property for application in unknown media. Another important feature is that the derived approach admits alternative hardware and software implementations and this flexibility can be adopted to enhance resilience to interference and eavesdropping. The performance of the optical-theorem-based detection method is discussed. It is shown that it outperforms existing methods for change detection such as the classical energy detector under certain coherence conditions or views of the imaging system. The derived theory and algorithms are illustrated with computer simulations.

关键词： Change detection distributed radar intrusion detection radar imaging optical theorem time reversal complex media

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Research on visualization Method for Detection Power of Guidance and Warning radar 7

Research on Visualization Method for Detection Power of Guid...

引用

7th IEEE Information technology and Mechatronics Engineering conference, ITOEC 2023

作者： Cheng, Dongsheng Xiang, Long Liu, Shihua Jiang, Wei Song, Ruoyu Air Force Early Warning Academy Wuhan China

ISBN: (纸本)9798350334197

The radar power map is a representation of radar detection performance. Due to the difficulty in measuring the power indicators of the guidance and warning radar, many radar units lack objective and accurate basis for evaluating the power of the radar during the use of radar equipment. The traditional radar power map drawing mainly relies on target aircraft flight inspection (inspection) data and mathematical model calculations. This article studies the method of correcting the free space power map based on a large amount of flight data accumulated after the deployment of radar positions. A method of correcting the detection distance and elevation of guidance and warning radar based on measured data is proposed, and the calculation method of radar detection probability based on measured data is discussed. A study was conducted on the methods for digitizing power maps. It has accurately reflected the radar detection performance, providing an effective reference for the radar to exert more combat effectiveness. Finally, the effectiveness of this correction method was verified through comparison. © 2023 IEEE.

关键词： visualization

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Depth Information Fusion Using radar-LiDAR-Camera Experimental Setup for ADAS Applications 13

Depth Information Fusion Using Radar-LiDAR-Camera Experiment...

引用

13th IEEE International conference on Communication Systems and Network Technologies, CSNT 2024

作者： Dayananda, N.B. Srivastava, Neetesh Achala, G. Nandagiri, Ashwin Srihari, Pathipati Pardhasaradhi, Bethi Cenkeramaddi, Linga Reddy National Institute of Technology Karnataka Ece Dept. Surathkal India Sri Shashaprayathi Technologies Pvt. Ltd. Surathkal Mangalore575025 India University of Agder Department of Information and Communication Technology Grimstad Norway

ISBN: (纸本)9798350305463

Improved scene perception makes the safe driving of automotive vehicles (AVs) feasible. The most common automotive sensors for AV perception for detection, classification, a nd t racking a re t he Light Detection And Ranging (LiDAR), radar, and camera sensors. The most reliable sensors for determining range are LiDAR and radar. In this research, we consider the referencing from the camera-based object recognition to fuse the LiDAR and radar point cloud data. To minimize any unintended effects from sensor orientation and sampling time, all three sensors are installed, calibrated, and time-aligned for this experiment. Subsequently, the obtained camera sensor data is subjected to object detection using a MobileNet-based deep neural network (DNN). The radar and LiDAR point cloud data are projected with the two-dimensional bounding box width, length, and height used for object recognition. Following that, the range information from the radar and LiDAR is retrieved and combined using a weighted average fusion algorithm. This experiment is run on the ROS platform, using AWR1642 radar sensor and RealSense LiDAR camera L515 sensor. The object detection from the camera and conducting fusion on the radar and LiDAR sensor is a potential algorithm for the Advanced Driver Assistant System (ADAS) emergency brake assistant (EBA) function. © 2024 IEEE.

关键词： Object recognition

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Feature Extraction using Variational Auto-encoder for radar-based Posture Detection Systems 99

Feature Extraction using Variational Auto-encoder for Radar-...

引用

IEEE 99th Vehicular technology conference (VTC-Spring)

作者： Casmin, Eugene Oliveira, Rodolfo Univ Nova Lisboa Fac Ciencias & Tecnol Dept Engn Electrotecn P-2829516 Caparica Portugal Inst Telecomunicacoes Aveiro Portugal

ISBN: (纸本)9798350387414

This paper explores the impact of different classification and feature engineering techniques in radar-based posture detection systems. The research focuses on an experimental setup involving a radar sensor used to classify human postures and the absence of human activity. The methodology includes the detection of the absence of human activity in the first stage, followed by feature engineering extraction through Variational Auto-Encoders (VAEs), and subsequent data reduction via either t-distributed Stochastic Neighbor Embedding (t-SNE) or Principal Component Analysis (PCA) if human activity is detected and posture classification is required. Five solutions differing in terms of methodology and features' dimensionality reduction strategies are presented and compared in terms of classification accuracy and computation time. The main findings reveal that all five solutions achieve classification accuracy exceeding 90%, with variations observed in computation times, showing the effectiveness of adopting VAEs to derive classification features from raw data. The research highlights a trade-off between data preservation and classification accuracy and/or computation time, demonstrating that feature extraction and data reduction techniques are a valuable enhancement to posture detection and classification.

关键词： Posture Classification radar Sensing VAEs tSNE PCA Performance Evaluation

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Enhanced Wind Turbine Detection through radar and Radiometric sensor Fusion

Enhanced Wind Turbine Detection through Radar and Radiometri...

引用

2024 Kleinheubach conference, KHB 2024

作者： Colak, Emre Chandra, Madhu Zichner, Ralf Sunar, Filiz Chemnitz University of Technology Faculty of Electrical Engineering and Information Technology Professorship of RF Technology and General Electrical Engineering Chemnitz09107 Germany Istanbul Technical University Civil Engineering Faculty Geomatics Engineering Department Maslak Istanbul80626 Turkey

ISBN: (纸本)9783948571122

This paper introduces an overview of a novel sensor fusion model for identifying wind turbines (WTs) that cause interference in polarimetric Doppler weather radar measurements. It is important to acknowledge that the characteristics of WTs, as defined by their radar cross section (RCS), have a substantial effect on radar performance. This is because the RCS of WTs leads to the introduction of multidimensional interference effects, which ultimately influence radar measurements. This interference is due to the incident electromagnetic fields that it interacts with a WT, which, in turn, generates both static and dynamic effects. In the realm of electromagnetic wave-matter interactions, the effective permittivity e f f is composed of two significant aspects: the real part varepsilon prime and the imaginary part varepsilon prime prime. In this context, passive satellite, also known as a radiometer provides sensor data (e.g. Sentinel-2 (S2)) that will be employed to accurately identify WTs causing interference to within the radar data. This is because the different effects of the real and imaginary components in metallic objects and the Earth's surface can be utilized to derive distinct radiometric satellite observables from the given S2 data. These radiometric observables lend themselves to serve as novel Wind Turbine Indices (WTI). Additionally, physical quantities such as emissivity epsilon λ and albedo rho albedo, which describe thermal radiation properties and reflectivity of surfaces, will be evaluated and applied to WT detection. © 2024 Deutscher Landesausschuss in der Bundesrepublik Deutschland e.V.

关键词： radar interference

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Feature screening of global targets for weather satellite radar 16

Feature screening of global targets for weather satellite ra...

引用

16th International conference on Microwave and Millimeter Wave technology (ICMMT)

作者： Feng, Minghao Shang, Jian Hu, Weidong Yuan, Mei Feng, Zhiyan Jia, Linhai Li, Yutong Beijing Inst Technol Beijing Peoples R China Natl Satellite Meteorol Ctr Natl Ctr Space Weather Beijing Peoples R China Innovat Ctr FengYun Meteorol Satellite FYSIC Beijing Peoples R China CMA Key Lab Radiometr Calibrat & Validat Environm Sat Beijing Peoples R China

ISBN: (纸本)9798350389968

The accurate calibration test of the first active remote sensing instrument, the FY-3E wind field measurement radar, relies on data provided by globally distributed stable targets. Utilizing data acquired from high-precision microwave radar scatterometers HY-2B SCAT, more than 107 regions around the world with stable backscattering coefficients are screened, including the Greenland, the Amazon rainforest, and the Congo Forest, to identify areas suitable for the on-orbit calibration test of the FY-3 series.

关键词： scatterometer calibration test natural target SCAT normalized standard deviation

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Automated radar data Labeling through Computer Vision

Automated Radar Data Labeling through Computer Vision

引用

IEEE Wireless and Microwave technology conference (WAMICON)

作者： Gabsteiger, Jasmin Maiwald, Timo Wuensche, Simon Weigel, Robert Lurz, Fabian Friedrich Alexander Univ Erlangen Nuremberg Inst Elect Engn Erlangen Germany Hamburg Univ Technol Inst High Frequency Technol Hamburg Germany

ISBN: (纸本)9798350398649

This paper presents the combination of an existing computer vision model with a highly integrated mm-wave radar system for automated radar data labeling. A pre trained model for person presence detection is used in combination with a camera sensor to determine, whether a person is present or not. The binary prediction is used to label simultaneously measured radar data of the same scenery. The paper addresses the challenges of varying illumination conditions, camera and radar sensor aperture angles and data quality which are dominant influences at the labeling process. The system significantly decreases human effort by automating the labeling process. It is used to generate 10,000 data points, label them and train a neural network with 95.7% accuracy. Finally, a proof-of-concept, training, and evaluation of radar-based activity detection with automatically labeled data is presented. The proposed method can contribute in person presence detection under challenging light conditions.

关键词： radar data labeling computer vision

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Experimental Application of FMCW AoPCB radar technology in Support of Maritime Autonomous Docking

Experimental Application of FMCW AoPCB Radar Technology in S...

引用

OCEANS conference

作者： Murray-Davis, Halie Bennett, Andrew Chordate Robot Lewes DE 19958 USA Point Rd Solut LLC Belmont MA USA

ISBN: (纸本)9798350362077

Recent investment into autonomous driving [1] has resulted in a multitude of technical advancements for mobile autonomous systems. Of particular note for maritime applications are improvements in sensing modalities, particularly radar sensors. Historically, maritime use has heavily relied on traditional radar sensing systems. These units are large, expensive, heavy, and require significant power to operate [2] [3]. In this paper we explore applying automotive-derived low cost, PCB mounted radar for autonomous maritime sensing applications. Our research indicates that these sensors perform effectively in cluttered maritime environments and are able to detect targets of interest such as docks, pilings, buoys, and other vessels. Integrating these sensors onto autonomous marine platforms is also feasible. We present both raw point cloud data displaying these objects from our field testing as well as the results of a software processing algorithm to identify such obstacles from radar sensor data.

关键词： Autonomous radar Docking Robot Boat FMCW AoPCB Maritime Marine data Collection

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sensor fusion based obstacle detection method for hazy environment 3

Sensor fusion based obstacle detection method for hazy envir...

引用

3rd International conference on sensors and Information technology (ICSI)

作者： Li, Yan Dong, Wei Qu, Jindai Gong, Tianyu Ji, Yan Gu, Miao Zhang, Tianqiang Liu, Yong Han, Guangsheng China FAW Grp Co Ltd Tianjin Peoples R China CATARC Tianjin Automot Engn Res Inst Co Ltd Tianjin Peoples R China

ISBN: (纸本)9781510666122;9781510666139

Based on camera and millimeter wave radar data fusion, an intelligent vehicle obstacle detection method suitable for hazy environment is proposed. Firstly, by taking the effectiveness of obstacle detection after image dehazing as the evaluation standard, a series of typical dehazing networks are compared and the best one was selected for image preprocessing. An obstacle detection model based on YOLOv5s depth network was established;Then, the camera data and radar data are fused in time and space, and the sensor data is associated based on the global nearest neighbor data association algorithm. Finally, the effectiveness of the proposed method is verified by open source data sets and real vehicle experiments.

关键词： sensor fusion Obstacle detection Image dehazing Millimeter wave radar

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MMA-Net: Multi-Modal Attention Network for 2-D Object Detection in Autonomous Driving

MMA-Net: Multi-Modal Attention Network for 2-D Object Detect...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gaur, Abhilash Goel, Shubh Goel, Kanishk Srirangarajan, Seshan Tseng, Po-Hsuan Feng, Kai-Ten New Delhi India Hsinchu Taiwan The Department of Electrical Engineering IIT Delhi New Delhi India The Department of Electrical Engineering Bharti School of Telecommunication Technology and Management IIT Delhi New Delhi India The Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan The Department of Electronics and Electrical Engineering NYCU Hsinchu Taiwan

ISBN: (纸本)9798350368741

Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multi-modal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator. © 2025 IEEE.

关键词： autonomous driving CARLA simulator multi-modal learning object detection sensor fusion

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