2023 IEEE Radar Conference (RadarConf23)最新文献_第2页

Direction Finding in Partly Calibrated Arrays Using Sparse Bayesian Learning 基于稀疏贝叶斯学习的部分校准阵列测向

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149551

Yihan Su, Guangbin Zhang, Tianyao Huang, Yimin Liu, Xiqin Wang

引用次数: 0

Time-Based Geolocation and Main Beam Estimation of an Airborne Rotating Radar for Spectrum Sharing 基于时间的机载旋转雷达频谱共享定位与主波束估计

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149570

L. Mailaender, A. Lackpour

{"title":"Time-Based Geolocation and Main Beam Estimation of an Airborne Rotating Radar for Spectrum Sharing","authors":"L. Mailaender, A. Lackpour","doi":"10.1109/RadarConf2351548.2023.10149570","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149570","url":null,"abstract":"Dynamic spectrum sharing between airborne radars and 5G cellular networks has the potential for granting additional RF spectrum to cellular networks while preserving the performance of airborne radars. In the case of an airborne radar with a predictably rotating antenna, a spectrum sharing controller can use estimates of the radar's location and beam orientation to anticipate and mitigate RF interference events over a large geographic area. However, localization of the radar is complicated by airborne radar's relatively narrow beamwidth and time-varying waveform. We introduce the Rotating Beam Time-of-Arrival (RB-TOA) algorithm to jointly estimate the radar's location and antenna main beam orientation. Each RF sensor is coarsely time-synchronized and measures the peak of the received signal envelope over each rotation interval to estimate when the radar's main beam maximally couples with the sensor's antenna; these time estimates are then combined at a sensor fusion server and the radar's main beam orientation and location are jointly solved using a gradient descent algorithm. We show that the RBTOA algorithm rapidly converges to a geolocation accuracy that is 50x better than the performance of a two-antenna angle-of-arrival algorithm (AoA) for the same number of sensors.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121846550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-Distance Bistatic Measurements of Space Object Motion using LOFAR Radio Telescope and Non-cooperative Radar Illuminator 基于LOFAR射电望远镜和非合作雷达照明器的空间目标运动远距离双基地测量

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149721

K. Jędrzejewski, M. Malanowski, K. Kulpa, M. Pożoga, A. Modrzewski, Michal Karwacki

引用次数: 0

Association of Camera and Radar Detections Using Neural Networks 使用神经网络的照相机和雷达探测协会

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149729

K. Fatseas, M. Bekooij

引用次数: 0

Experimental comparison of Starlink and OneWeb signals for passive radar 无源雷达Starlink和OneWeb信号的实验比较

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149580

R. Blázquez-García, D. Cristallini, M. Ummenhofer, V. Seidel, J. Heckenbach, D. O’Hagan

引用次数: 2

A Fast 2D Super-resolution Imaging Method via Bayesian Compressive Sensing for mmWave Automotive radar 基于贝叶斯压缩感知的毫米波汽车雷达快速二维超分辨率成像方法

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149712

Yanqin Xu, Yuan Song, Shunjun Wei, Xiaoling Zhang, Lanwei Guo, Xiaowo Xu

{"title":"A Fast 2D Super-resolution Imaging Method via Bayesian Compressive Sensing for mmWave Automotive radar","authors":"Yanqin Xu, Yuan Song, Shunjun Wei, Xiaoling Zhang, Lanwei Guo, Xiaowo Xu","doi":"10.1109/RadarConf2351548.2023.10149712","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149712","url":null,"abstract":"Millimeter-wave (mmW) automotive radar imaging technology is widely applied to advanced driver assistance systems (ADAS). Existing super-resolution imaging methods can improve angular resolution for automotive radar with a limited aperture. However, these super-resolution methods have high computational complexity, meanwhile have poor imaging performance in single-snapshot. To address these problems. we propose a fast 2D super-resolution imaging method for real-time and high-quality automotive radar imaging. First, a novel Bayesian compressive sensing with the Kailath-Variant (BCS-KV) imaging method is proposed to achieve superior angular super-resolution in single-snapshot. And the K-V is used to reduce the complexity of matrix inversion. Then, in the range dimension, a Multi-Channel Accumulation (MCA) is utilized to detect the effective range unit to further reduce the 2D imaging computational complexity. Finally, both simulated and experimental results demonstrate that the proposed method has lower computational complexity and compelling imaging performance than other imaging methods.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134143017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Covariance Matrix Estimation With Kronecker Structure Constraint For Polarimetric Detection 基于Kronecker结构约束的偏振检测协方差矩阵估计

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149563

Jiaheng Wang, Yalong Wang, Haoqi Wu, Zhihang Wang, Jun Yu Li

{"title":"Covariance Matrix Estimation With Kronecker Structure Constraint For Polarimetric Detection","authors":"Jiaheng Wang, Yalong Wang, Haoqi Wu, Zhihang Wang, Jun Yu Li","doi":"10.1109/RadarConf2351548.2023.10149563","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149563","url":null,"abstract":"With the Kronecker product structure constraint, this paper proposes a covariance matrix (CM) estimation method in the Compound-Gaussian (CG) sea clutter background. We assume the CG clutter in different polarization channels has different textures, which is different from the existing Kronecker structure-based CM estimation methods for polarimetric target detection. Based on the maximum likelihood (ML) criterion, we obtain the fixed point equation of the CM and solve it by an iterative algorithm. The proposed method is referred to as the Kronecker-based maximum likelihood estimate (KMLE), and the relevance of KMLE to the existing estimation methods is also discussed. For the performance assessment, we demonstrate the estimation accuracy of KMLE by presenting the normalized mean-square error (NMSE), and the detection performance is assessed by inserting the estimated CM into the test statistic of the texture-free generalized likelihood ratio test (TF-GLRT) detector. Through simulations with the synthetic and real sea clutter, we verify that KMLE outperforms other estimation methods when the training samples are limited.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134362225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semi-Supervised Active Learning for Radar based Object Classification Using Track Consistency 基于跟踪一致性的雷达目标分类半监督主动学习

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149705

Johannes Benz, Christian Weiss, Axel Acosta Aponte, Gor Hakobyan

{"title":"Semi-Supervised Active Learning for Radar based Object Classification Using Track Consistency","authors":"Johannes Benz, Christian Weiss, Axel Acosta Aponte, Gor Hakobyan","doi":"10.1109/RadarConf2351548.2023.10149705","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149705","url":null,"abstract":"Development of machine learning (ML) models requires large amounts of labeled data. For safety critical automotive applications such as radar based perception, the dataset must contain various and rare corner cases, e.g. rare instances that have not been seen before. The straightforward approach of measuring and manually labeling large amounts of data to capture such corner cases is often infeasible or impractical. Thus, approaches for efficiently selecting and labeling the relevant data are essential for ML-based radar applications. In this paper, we propose a method for semi-supervised learning (SSL) for radar object type classification. We use the track consistency of tracked radar objects as a constraint to generate high-quality labels for the vast portions of the unlabeled dataset. We extend the proposed SSL approach with active learning that considers the data relevance, such that the most relevant data with the least accurate auto-labels are selected for human labeling. We show that the proposed approach achieves a saving of more than 87% of human labeling costs based on auto-labeling and relevant data selection.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134407817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of Waveform Parameters for Multiple Target Tracking Systems in Cognitive Radars 认知雷达中多目标跟踪系统的波形参数优化

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149572

Taylan Denizcan Çaha, L. D. Ata

{"title":"Optimization of Waveform Parameters for Multiple Target Tracking Systems in Cognitive Radars","authors":"Taylan Denizcan Çaha, L. D. Ata","doi":"10.1109/RadarConf2351548.2023.10149572","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149572","url":null,"abstract":"In this study, cognitive radar (CR) applications including radar waveform parameters and track update interval selection are investigated in order to balance the time resource cost and increase the accuracy performance of multiple target tracking systems. For the target tracking part, the unscented Kalman filter (UKF) is applied together with the joint probabilistic data association (JPDA) and the interacting multiple models (IMM) algorithm, which is used to realize more than one target motion model. The waveform parameters and track update interval are adaptively updated by using the outputs of the radar data processing block including target tracking and classification algorithms. The waveform parameters to be updated, the product of the pulse width and the number of integrated pulses, and the track update interval are selected. In the optimization function, the limit values of the parameter selections are decided by using target class information which is supplied by a random forest classifier. Along with the proposed cost function, track continuity and time resource allocation are tested and system performance is demonstrated depending on the target characteristics. In the simulations part, multiple target scenarios that include targets with different maneuvers and radar cross sections (RCS) have been examined and it is shown that the proposed cost function can be applied in multiple target tracking scenarios.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131636745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable Artificial Intelligence based Classification of Automotive Radar Targets 基于可解释人工智能的汽车雷达目标分类

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149788

Neeraj Pandey, S. S. Ram

引用次数: 0