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Your search keyword '"Huang, Yulin"' showing total 408 results
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408 results on '"Huang, Yulin"'

2. A Hybrid Real/Synthetic Aperture Scheme for Multichannel Radar Forward-Looking Superresolution Imaging.

Author

Li, Wenchao, Chen, Rui, Yang, Jianyu, Wu, Junjie, Zhang, Yin, and Huang, Yulin

Abstract

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. Although the ambiguity can be resolved using multiple channels in azimuth, its azimuth resolution in the vicinity of flight path is usually poor due to the small angle variation. To solve the above problems, a hybrid real/synthetic aperture scheme for multichannel radar forward-looking imaging is proposed in this letter. In the scheme, the synthetic aperture imaging result with left/right ambiguity is obtained first using the information of platform motion. Then the real aperture superresolution imaging with regularized iterative adaptive approach (RIAA) is achieved using the instantaneous data of multiple channels. Finally, the two imaging results are fused to obtain the forward-looking superresolution image without left/right ambiguity. Simulation results are given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Published
2023
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3. DSNN: A Dynamic-Structure Neural Network for Aerial Target Multiview High-Resolution Range Profiles Classification.

Author

Lu, Yuchun, Pei, Jifang, Wang, Xiangcheng, Zhang, Yin, Huo, Weibo, Huang, Yulin, and Yang, Jianyu

Abstract

Multiview high-resolution range profiles (HRRPs) of aerial targets contain more target information than single-view one and will benefit accurate classification. However, feature information in HRRPs dynamically varies across different views, thus a dynamic classification framework is required to adjust the structure of the network along with the feature information variations and effectively make full use of those multiview features. To this end, we propose a dynamic-structure neural network (DSNN) with skip extraction and adaptive fusion blocks to adjust the network structure and adaptively fuse multiview features, enabling accurate aerial target HRRPs classification. In the skip extraction block, the skip gate automatically changes the block depth of each view to fit feature information variations, which ensures multiview HRRP features are dynamically exploited and extracted by the network. Then, in the adaptive fusion block, features from different views are weighted by the adaptive weight gate and effectively fused using associated attention, which further contributes to the classification. Besides, since the skip gate dynamically downsizes the extraction block for some views, the computational cost of DSNN is also reduced to some extent. Experimental results demonstrate that the proposed method has superior aerial target multiview HRRPs classification performance and computational efficiency over other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Published
2023
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4. Sparse Target Batch-Processing Framework for Scanning Radar Superresolution Imaging.

Author

Tuo, Xingyu, Mao, Deqing, Zhang, Yin, Zhang, Yongchao, Huang, Yulin, and Yang, Jianyu

Abstract

Sparse superresolution algorithms have been applied in scanning radar imaging to improve its azimuth resolution. However, the inverse matrix in each iteration is usually diagonal loading by the updating result, which leads to huge computational complexity for 2-D echo data. In this letter, a batch-processing superresolution framework is proposed to process the echo data in parallel. On the one hand, the optimization problem for sparse target recovery is modified as matrix form, which presents the batch-processing potential for 2-D echo data. On the other hand, the optimization problem is solved by the proposed alternating direction method of multipliers (ADMM)-based batch-processing framework, which can avoid high-dimensional matrix inversion along different range bins. Compared with traditional sparse superresolution methods, the proposed batch-processing framework is more suitable for 2-D echo data superresolution. [ABSTRACT FROM AUTHOR]

Published
2023
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5. An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR.

Author

Wang, Chenwei, Luo, Siyi, Pei, Jifang, Liu, Xiaoyu, Huang, Yulin, Zhang, Yin, and Yang, Jianyu

Abstract

Existing synthetic aperture radar automatic target recognition (SAR ATR) methods have been effective for the classification of seen target classes. However, it is more meaningful and challenging to distinguish the unseen target classes, i.e., open set recognition (OSR) problem, which is an urgent problem for the practical SAR ATR. The key solution of OSR is to effectively establish the exclusiveness of feature distribution of known classes. In this letter, we propose an entropy-awareness meta-learning method that improves the exclusiveness of feature distribution of known classes which means our method is effective for not only classifying the seen classes but also encountering the unseen other classes. Through meta-learning tasks, the proposed method learns to construct a feature space of the dynamic-assigned known classes. This feature space is required by the tasks to reject all other classes not belonging to the known classes. At the same time, the proposed entropy-awareness loss helps the model to enhance the feature space with effective and robust discrimination between the known and unknown classes. Therefore, our method can construct a dynamic feature space with discrimination between the known and unknown classes to simultaneously classify the dynamic-assigned known classes and reject the unknown classes. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown the effectiveness of our method for SAR OSR. [ABSTRACT FROM AUTHOR]

Published
2023
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23. NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

Author

Wang, Longguang, primary, Guo, Yulan, additional, Wang, Yingqian, additional, Li, Juncheng, additional, Gu, Shuhang, additional, Timofte, Radu, additional, Chen, Liangyu, additional, Chu, Xiaojie, additional, Yu, Wenqing, additional, Jin, Kai, additional, Wei, Zeqiang, additional, Guo, Sha, additional, Yang, Angulia, additional, Zhou, Xiuzhuang, additional, Guo, Guodong, additional, Dai, Bin, additional, Peng, Feiyue, additional, Xiao, Huaxin, additional, Yan, Shen, additional, Liu, Yuxiang, additional, Cai, Hanxiao, additional, Cao, Pu, additional, Nie, Yang, additional, Yang, Lu, additional, Song, Qing, additional, Hu, Xiaotao, additional, Xu, Jun, additional, Xu, Mai, additional, Jing, Junpeng, additional, Deng, Xin, additional, Xing, Qunliang, additional, Qiao, Minglang, additional, Guan, Zhenyu, additional, Guo, Wenlong, additional, Peng, Chenxu, additional, Chen, Zan, additional, Chen, Junyang, additional, Li, Hao, additional, Chen, Junbin, additional, Li, Weijie, additional, Yang, Zhijing, additional, Li, Gen, additional, Li, Aijin, additional, Sun, Lei, additional, Zhang, Dafeng, additional, Liu, Shizhuo, additional, Zhang, Jiangtao, additional, Qu, Yanyun, additional, Yang, Hao-Hsiang, additional, Huang, Zhi-Kai, additional, Chen, Wei-Ting, additional, Chang, Hua-En, additional, Kuo, Sy-Yen, additional, Liang, Qiaohui, additional, Lin, Jianxin, additional, Wang, Yijun, additional, Yin, Lianying, additional, Zhang, Rongju, additional, Zhao, Wei, additional, Xiao, Peng, additional, Xu, Rongjian, additional, Zhang, Zhilu, additional, Zuo, Wangmeng, additional, Guo, Hansheng, additional, Gao, Guangwei, additional, Zeng, Tieyong, additional, Pi, Huicheng, additional, Zhang, Shunli, additional, Kim, Joohyeok, additional, Kim, HyeonA, additional, Park, Eunpil, additional, Sim, Jae-Young, additional, Zhai, Jucai, additional, Zeng, Pengcheng, additional, Liu, Yang, additional, Ma, Chihao, additional, Huang, Yulin, additional, and Chen, Junying, additional

Published
2022
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24. NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Author

Yang, Ren, primary, Timofte, Radu, additional, Zheng, Meisong, additional, Xing, Qunliang, additional, Qiao, Minglang, additional, Xu, Mai, additional, Jiang, Lai, additional, Liu, Huaida, additional, Chen, Ying, additional, Ben, Youcheng, additional, Zhou, Xiao, additional, Fu, Chen, additional, Cheng, Pei, additional, Yu, Gang, additional, Li, Junyi, additional, Wu, Renlong, additional, Zhang, Zhilu, additional, Shang, Wei, additional, Lv, Zhengyao, additional, Chen, Yunjin, additional, Zhou, Mingcai, additional, Ren, Dongwei, additional, Zhang, Kai, additional, Zuo, Wangmeng, additional, Ostyakov, Pavel, additional, Dmitry, Vyal, additional, Soltanayev, Shakarim, additional, Sergey, Chervontsev, additional, Magauiya, Zhussip, additional, Zou, Xueyi, additional, Yan, Youliang, additional, Michelini, Pablo Navarrete, additional, Lu, Yunhua, additional, Zhang, Diankai, additional, Liu, Shaoli, additional, Gao, Si, additional, Wu, Biao, additional, Zheng, Chengjian, additional, Zhang, Xiaofeng, additional, Lu, Kaidi, additional, Wang, Ning, additional, Canh, Thuong Nguyen, additional, Bach, Thong, additional, Wang, Qing, additional, Sun, Xiaopeng, additional, Ma, Haoyu, additional, Zhao, Shijie, additional, Li, Junlin, additional, Xie, Liangbin, additional, Shi, Shuwei, additional, Yang, Yujiu, additional, Wang, Xintao, additional, Gu, Jinjin, additional, Dong, Chao, additional, Shi, Xiaodi, additional, Nian, Chunmei, additional, Jiang, Dong, additional, Lin, Jucai, additional, Xie, Zhihuai, additional, Ye, Mao, additional, Luo, Dengyan, additional, Peng, Liuhan, additional, Chen, Shengjie, additional, Liu, Xin, additional, Wang, Qian, additional, Liang, Boyang, additional, Dong, Hang, additional, Huang, Yuhao, additional, Chen, Kai, additional, Guo, Xingbei, additional, Sun, Yujing, additional, Wu, Huilei, additional, Wei, Pengxu, additional, Huang, Yulin, additional, Chen, Junying, additional, Hyun Lee, Ik, additional, Ali Khowaja, Sunder, additional, and Yoon, Jiseok, additional

Published
2022
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44. Dynamic modeling of a stick-slip driving smart surface platform

Author

Li Pengchao, Huang Yulin, and Huyan Pengfei

Subjects
Surface (mathematics), Dynamic simulation, Computer science, Stroke (engine), Control parameters, Coulomb friction, Actuator, Simulation, ComputingMethodologies_COMPUTERGRAPHICS, System dynamics, Slip (vehicle dynamics)
Abstract

This paper is focused on the dynamic modeling of a stick-slip driving smart surface platform. A dynamic Simulink model based on Coulomb friction model and mass center shifting has been created and presented. Another dynamic simulation in ADAMS has also been presented for comparison. With the models, the influences of several control parameters to the output stroke of the smart surface platform have been studied. The simulation results are presented and discussed.

Published
2021
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