2023 IEEE Radar Conference (RadarConf23)最新文献_第3页

A Parallel Dual-Task Learning Network for InSAR Phase Retrieval InSAR相位检索的并行双任务学习网络

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149761

Xu Zhan, Xiaoling Zhang, Xiangdong Ma, Jun Shi, Shunxin Zheng, Jiaping Chen, Shunjun Wei, Tianjiao Zeng

{"title":"A Parallel Dual-Task Learning Network for InSAR Phase Retrieval","authors":"Xu Zhan, Xiaoling Zhang, Xiangdong Ma, Jun Shi, Shunxin Zheng, Jiaping Chen, Shunjun Wei, Tianjiao Zeng","doi":"10.1109/RadarConf2351548.2023.10149761","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149761","url":null,"abstract":"This work focuses on the problem of InSAR phase retrieval. Current methods consist of two cascaded tasks: phase filtering and phase unwrapping. Unavoidable accumulated errors cause precision loss, and serial computations cause efficiency loss. We propose a parallel dual-task learning work to address these issues for high-quality and efficient InSAR phase retrieval. Methodologically, we retrieve the InSAR phase in a parallel manner instead of a serially cascaded one. Specifically, three core phases throughout the whole processing chain are considered, including feature attraction, task learning, and task balance. First, for feature attraction, considering the InSAR image characteristics, we propose a hybrid Trans-Encoder module to attract features locally and nonlocally. Second, regarding the dual-task needs for feature learning, we propose a dual-decoder to denoise and unwrap parallelly. Third, considering the dual-task's different attributes for task balance, we propose an uncertainty-weighted loss to make balances between tasks. Experiments on both simulated and measured data verify the proposed method's higher precision and efficiency compared to other methods. An ability study is conducted that confirms the effectiveness of the proposed modules.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"27 3 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":"131048207","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

Adaptive Radar Subarray Scheduling 自适应雷达子阵列调度

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149581

K. Wagner, Taylor George

引用次数: 0

A Robust Framework to Design Optimal Radar Deployment for Range-Based Target Localization Technique 基于距离的目标定位技术中最优雷达部署设计的鲁棒框架

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149644

A. Aubry, P. Babu, A. De Maio, Ghania Fatima, Nitesh Sahu

{"title":"A Robust Framework to Design Optimal Radar Deployment for Range-Based Target Localization Technique","authors":"A. Aubry, P. Babu, A. De Maio, Ghania Fatima, Nitesh Sahu","doi":"10.1109/RadarConf2351548.2023.10149644","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149644","url":null,"abstract":"In this paper, the problem of designing the optimal positions of monostatic radars composing a multiplatform network is pursued. Leveraging the CRB of the target position based on radar range measurements, two different figures of merit (independent of the actual target location) are considered. Unlike the state-of-the-art techniques, which usually rely on the restrictive assumption that the target is at the center of the sensing region and study the determination of the optimal angular orientation of the nodes, a new approach to design the optimal radar deployment (without knowing target location) is developed. Specifically, a region where the target is likely to be present is considered and either the trace of the CRB averaged over the grid points sampling the surveillance area (shortly average CRB), or the maximum trace of CRB over the mentioned grid points (shortly worst-case CRB) is minimized. Hence, an optimization framework based on block majorization-minimization (referred to as block-MM) is proposed to deal with the formulated resource allocation problems. Remarkably, regardless of the considered figures of merit, the design objective decreases monotonically along the iteration steps of the proposed algorithm. The developed methodology can also efficiently handle the case of nonuniform measurement noise. Finally, via numerical simulations, the effectiveness of the developed methods is shown.","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":"123520834","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

Optimal Subspace Estimation in Radar Signal Processing 雷达信号处理中的最优子空间估计

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149565

K. Adhikari, R. Vaccaro, Ridhab K. Al Kinani

引用次数: 1

Compensating Power Amplifier Distortions on Radar Signals via Waveform Design 利用波形设计补偿雷达信号中的功率放大器畸变

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149753

E. Raei, M. Alaee-Kerahroodi, B. M. R., B. Ottersten

引用次数: 0

Holographic Radar: Optimal Beamformer Design for Detection Accuracy Maximization 全息雷达:探测精度最大化的最佳波束形成器设计

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149724

Haobo Zhang, Hongliang Zhang, Boya Di, Zhu Han, Lingyang Song

引用次数: 0

HA-SARSD: An Effective SAR Ship detector via the Hybrid Attention Residual Module HA-SARSD:一种有效的基于混合注意残差模块的SAR舰船探测器

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149642

Nanjing Yu, H. Ren, Tianmin Deng, Xiaobiao Fan

引用次数: 1

Matched Filtering Performance Analysis for Massive MIMO Radar with One-Bit Quantization 一比特量化大规模MIMO雷达匹配滤波性能分析

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149768

Minglong Deng, Haoqi Wu, Ziyang Cheng, Jiaheng Wang, Zishu He

引用次数: 0

A Study of Practical Radar-based Nighttime Respiration Monitoring at Home 基于雷达的家庭夜间呼吸监测的实用研究

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149560

Yindong Hua, Zongxing Xie, Fan Ye

{"title":"A Study of Practical Radar-based Nighttime Respiration Monitoring at Home","authors":"Yindong Hua, Zongxing Xie, Fan Ye","doi":"10.1109/RadarConf2351548.2023.10149560","DOIUrl":"https://doi.org/10.1109/RadarConf2351548.2023.10149560","url":null,"abstract":"Radar-based solutions support practical and longitudinal respiration monitoring owing to their non-invasive nature. Nighttime respiration monitoring at home provides rich and high-quality data, mostly free of motion disturbances because the user is quasi-stationary during sleep, and 6–8 hours per day rather than tens of minutes, promising for longitudinal studies. However, most existing work was conducted in laboratory environments for short periods, thus the environment, user motions, and postures can differ significantly from those in real homes. To understand how to obtain quality, overnight respiration data in real homes, we conduct a thorough experimental study with 6 participants of various sleep postures over 9 nights in 4 real-home testbeds, each configured with 3–4 sensors around the bed. We first compare the performance among four typical sensor placements around the bed to understand which is the optimal location for high quality data. Then we explore methods to track range bins with high quality signals as occasional user motions change the distance thus signal qualities, and different aspects of amplitude and phase data to further improve the signal quality using metrics of the periodicity-to-noise ratio (PNR) and end-to-end (e2e) accuracy. The experiments demonstrate that the sensor placement is a vital factor, and the bedside is an optimal choice considering both accuracy and ease of deployment (2.65 bpm error at 80 percentile), also consistent among four typical sleep postures. We also observe that, a proper range bin selection method can improve the PNR by 2 dB at 75-percentile, and e2e accuracy by 0.9 bpm at 80-percentile. Both amplitude and phase data have comparable e2e accuracy, while phase is more sensitive to motions thus suitable for nighttime movement detection. Based on these discoveries, we develop a few simple practical guidelines useful for the community to achieve high quality, longitudinal home-based overnight respiration monitoring.","PeriodicalId":168311,"journal":{"name":"2023 IEEE Radar Conference (RadarConf23)","volume":"10 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":"123887071","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

Improved Implementation of the Frequency Hopped Code Selection DFRC Scheme 跳频码选择DFRC方案的改进实现

2023 IEEE Radar Conference (RadarConf23) Pub Date : 2023-05-01 DOI: 10.1109/RadarConf2351548.2023.10149725

E. Aboutanios, William Baxter, Yimin D. Zhang

引用次数: 0