张柘

中国科学院空天信息创新研究院
中科院空天院 苏州研究院 / 苏州空天信息研究院
研究员,博士生导师

入选国家海外高层次人才引进计划,中科院高层次人才引进计划,苏州市“姑苏领军”人才

电子邮件: zhangzhe01@aircas.ac.cn
通信地址: 江苏省苏州市工业园区独墅湖大道158号中国科学院空天信息研究院
邮政编码: 215000
联系电话: 0512-69836908
Office:      空天院苏研院22室B203-12

Google scholar:https://scholar.google.com/citations?hl=en&user=dsDkKTkAAAAJ
Github:    https://github.com/pzhg
CV:             CV of Zhe

研究领域

稀疏信号处理:包括凸优化、稀疏表征、原子范数优化等新兴理论
稀疏微波成像:稀疏信号处理理论在微波二维成像、三维成像中的应用及对应的新体制雷达技术
信号处理与深度学习的结合:包括神经网络的可解释性、混合神经网络、信号处理方法在神经网络中的应用等

招生信息

  • 我这个人不太会push别人,我倒是希望你push我
  • 我这个人不太会使唤学生,也不太会故作威严,我希望能和所有学生成为可以长久相交的好友
  • 你可以想去工业界,也可以想进学术界,实在想不明白,也可以抓着我商量

我对学生只有一个要求:请对科研怀有热情、心存敬畏、充满主动和干劲

招生专业
081002-信号与信息处理
招生方向
稀疏信号处理、稀疏微波成像与新体制合成孔径雷达成像理论
信号处理与深度学习的结合及其在微波成像中的应用

教育背景

2014-01--2014-04   康涅狄格大学   访问学生
2009-09--2015-07   中国科学院大学 中国科学院电子学研究所   工学博士
2004-09--2008-07   西安交通大学   工学学士
2003-09--2004-07   西安交通大学   少年班

工作经历

   
工作简历
2023-01~现在, 中国科学院空天信息创新研究院/苏州空天信息研究院, 22室主任助理/学术带头人
2022-04~现在, 中国科学院空天信息创新研究院/苏州空天信息研究院, 研究员
2021-01~2022-04,中国科学院空天信息创新研究院/苏州空天信息研究院, 副研究员/创新研究员
2016-12~2020-06,乔治梅森大学, 博士后研究员
2015-12~2016-11,乔治华盛顿大学, 博士后研究员
社会兼职
2022-07-10-今,校外导师, 西交利物浦大学
2022-01-01-今,学术会议组委会成员及学术主席, MiViSAR2022会议组委会成员及学术程序主席
2020-12-01-今,期刊审稿人, IEEE Geoscience and Remote Sensing Letters
2020-12-01-今,期刊审稿人, 雷达学报
2015-12-31-今,期刊审稿人, IET Radar, Sonar & Navigation
2015-12-31-今,期刊审稿人, IET Electronics Letters
2015-12-31-今,期刊审稿人, IET Signal Processing
2015-12-31-今,期刊审稿人, IEEE Transactions on Signal Processing
2015-04-30-今,学术会议TPC成员, 为CoSeRa2015、CoSeRa2016、CoSeRa2018、SIRS2020等近30个国际领域知名学术会议担任学术委员会(TPC)成员或审稿人
主持开源项目

兵马俑BBS (http://bbs.xjtu.edu.cn/, http://bmybbs.com/)
项目托管:https://github.com/bmybbs?type=source
技术负责人
(2006.4~2015.11)
兵马俑BBS 是中国教育网最大的BBS 站点之一,属于西安交通大学。兵马俑 BBS 服务50000 多名用户,同时在线数最高5000。 项目代码超过10 万行。

科苑星空BBS (http://bbs.ucas.ac.cn/, http://kyxk.net/)
技术负责人
(2010.6~2019.4)

科苑星空BBS 是中国科学院的官方社区。科苑星空BBS 服务30000 多名用户, 同时在线数最高1500. 项目代码超过12 万行。

hCNN 项目
项目托管:https://github.com/pzhg/hCNN
hCNN 是一个开源的、深度学习与信号处理结合的开发框架,可方便的进行深
度学习开发与训练,并与传统信号处理方法兼容.

专利与奖励


奖励信息
(1) 全国颠覆性技术创新大赛, , 国家级, 2021
专利成果
  1. 基于PhaseLift自聚焦算法的稀疏微波成像方法, 发明专利, 2017, 第 1 作者, 专利号: CN104808205B
  2. 基于l q 正则化的偏置相位中心天线成像方法, 发明专利, 2016, 第 4 作者, 专利号: CN105929397A
  3. 一种基于相位恢复的稀疏微波成像自聚焦方法及装置, 发明专利, 2015, 第 1 作者, 专利号: CN104749571A
  4. 一种基于稀疏度估计的分维度阈值迭代稀疏微波成像方法, 发明专利, 2014, 第 5 作者, 专利号: CN104251991A
  5. ​装载于慢速平台上的成像雷达的稀疏微波成像方法及装置, 发明专利, 2013, 第 4 作者, 专利号: CN103197312A

出版信息

   
发表论文

Under Review
  1. M. Wang, Z. Zhang*, X. Qiu, S. Gao and Y. Wang, “ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold,” IEEE Trans. Geosci. Remote Sens., under review.
  2. R. Shi, Z. Zhang*, X. Qiu, and C. Ding, “A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient Gridless Line Spectrum Estimation with Applications in Tomographic SAR Imaging,” IEEE Trans. Geosci. Remote Sens., under review.
  3. J. Li, Z. Xu, Z. Li, Z. Zhang*, B. Zhang, and Y. Wu, “An Unsupervised CNN-based Multichannel Interferometric Phase Denoising Method Applied to TomoSAR Imaging,”  IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., under review.
  4. P. Jiang, Z. Zhang*, and B. Zhang, “A novel TomoSAR imaging method with few observations based on nested array,”  IET Radar Sonar & Navigation, under review.
  5. Y. Zhao, Y. Chen, S. Xie*, L. Cui, and Z. Zhang*, “Wide Angle SAR Imaging Method Based on Blind Compressed Sensing,”  Remote Sensing, under review.
  6. J. Kang*, T. Ji, Z. Zhang, and R. Fernandez-Beltran, “SAR Time Series Despeckling via Nonlocal Matrix Decomposition in Logarithm Domain,” Signal Processing, under review.
  7. 康健, 童风雨, 白雨松, 丁翔, 冀腾宇, and 张柘, “基于对数域矩阵分解的时序SAR图像相干斑抑制及成分分析方法,“ 雷达学报, 投稿中.

2022

  1. X. Ding, J. Kang*, Z. Zhang, Y. Huang, J. Liu, and N. Yokoya, “Coherence-Guided Complex Convolutional Sparse Coding for Interferometric Phase Restoration,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–14, 2022, doi: 10.1109/TGRS.2022.3228279.
  2. Z. Zhu, J. Kang*, T. Ji, Z. Zhang, and R. Fernandez-Beltran, “SAR Time-Series Despeckling via Nonlocal Total Variation Regularized Robust PCA,” IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1–5, 2022, doi: 10.1109/LGRS.2022.3227187.
  3. P. Jiang, Z. Zhang*, and B. Zhang, “Efficient Sparse MIMO SAR Imaging with Fast Iterative Method Based on Back Projection and Approximated Observation,” in 2022 5th International Conference on Electronics and Electrical Engineering Technology (EEET 2022), 2022, pp. 1–6.
  4. S. Gao, Z. Zhang*, B. Zhang, and Y. Wu, “Gridless Tomographic SAR Imaging Based On Accelerated Atomic Norm Minimization with Efficiency,” in International Conference on Radar Systems 2022, 2022, pp. 1–6.
  5. M. Wang, Z. Zhang*, Y. Wang, S. Gao, and X. Qiu, “TomoSAR-ALISTA : Efficient TomoSAR Imaging via Deep Unfolded Network,” in International Conference on Radar Systems 2022, 2022, pp. 1–6.
  6. Z. Xu, G. Zhou, B. Zhang, Z. Zhang, and Y. Wu, “An Accurate Sparse SAR Imaging Method for Joint Feature Enhancement Based on Nonconvex-Nonlocal Total Variation Regularization,” in 14th European Conference on Synthetic Aperture Radar (EUSAR 2022), 2022, pp. 576–581. [Online]. Available: https://ieeexplore.ieee.org/document/9944320.
  7. Z. Xu, B. Zhang, Z. Zhang*, M. Wang, and Y. Wu, “Nonconvex-Nonlocal Total Variation Regularization Based Joint Feature-Enhanced Sparse SAR Imaging,” IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1–1, 2022, doi: 10.1109/lgrs.2022.3222185.
  8. Y. Zhao, W. Huang, X. Quan, W.-K. Ling, and Z. Zhang*, “Data-driven sampling pattern design for sparse spotlight SAR imaging,” Electron. Lett., Oct. 2022, doi: 10.1049/ELL2.12650.
  9. 吕泽鑫, 仇晓兰*, 张柘, and 丁赤飚, “极化干涉SAR面向城区不同处理模式的误差影响分析,” 雷达学报, 2022, doi: 10.12000/JR22059.
  10. Z. Xu, G. Zhou, B. Zhang, Z. Zhang*, and Y. Wu, “Sparse regularization method combining SVA for feature enhancement of SAR images,” Electron. Lett., Jun. 2022, doi: 10.1049/ell2.12509.
  11. 赵曜, 许俊聪, 全相印, 崔莉, and 张柘*, “基于稀疏和低秩结构的层析SAR成像方法,” 雷达学报, vol. 11, no. 1, pp. 52–61, 2022, doi: 10.12000/JR21210.
  12. 杜邦, 仇晓兰, 张柘, 雷斌, and 丁赤飚, “基于扰动的结合Off-grid目标的层析SAR三维成像方法,” 雷达学报, vol. 11, no. 1, pp. 62–70, 2022, doi: 10.12000/JR21093.
2021
  1. Z. Zhang et al., “The First Airborne Experiment of Sparse Microwave Imaging: Prototype System Design and Result Analysis,” no. 1, Oct. 2021, Accessed: Oct. 22, 2021. [Online]. Available: https://arxiv.org/abs/2110.10675v1
  2. M. Liu, J. Li, Z. Zhang, B. Zhang, and Y. Wu, “Azimuth Ambiguities Suppression for Multichannel SAR Imaging Based on L2,q Regularization: Initial Results of Non-sparse Scenario,” in International Geoscience and Remote Sensing Symposium (IGARSS) 2021, 2021, pp. 3153–3156.
  3. B. Du, Z. Zhang, X. Qiu, B. Lei, and C. Ding, “Multi-aspect Tomographic SAR Imaging Approach via Distributed Compressed Sensing and Joint Sparsity,” in CIE Radar Conference 2021, 2021, pp. 2–5.
2020
  1. Z. Zhang et al., “Embedded micro radar for pedestrian detection in clutter,” in 2020 IEEE International Radar Conference, RADAR 2020, Apr. 2020, pp. 368–372. doi: 10.1109/RADAR42522.2020.9114544.
2019
  1. P. Xu, Z. Tian, Z. Zhang, and Y. Wang, “Coke: Communication-Censored Kernel Learning Via Random Features,” 2019 IEEE Data Sci. Work. DSW 2019 - Proc., pp. 32–36, Jun. 2019, doi: 10.1109/DSW.2019.8755802.
  2. Z. Zhang, Y. Wang, and Z. Tian, “Efficient two-dimensional line spectrum estimation based on decoupled atomic norm minimization,” Signal Processing, vol. 163, no. Xx, pp. 95–106, Oct. 2019, doi: 10.1016/j.sigpro.2019.04.024.
  3. Z. Wang, X. Lin, X. Xiang, Z. Zhang, Z. Tian, K. Pham, E. Blasch, and G. Chen, “A hidden chamber detector based on a MIMO SAR,” in Sensors and Systems for Space Applications XII, Jul. 2019, vol. 11017, no. 29, p. 6. doi: 10.1117/12.2520643.
2018
  1. Z. Zhang, X. Chen, and Z. Tian, “A hybrid neural network framework and application to radar automatic target recognition,” 2018 IEEE Glob. Conf. Signal Inf. Process. Glob. 2018 - Proc., pp. 246–250, 2018, doi: 10.1109/GlobalSIP.2018.8646582.
2017
  1. Z. Zhang and Z. Tian, “ANM-PhaseLift: Structured line spectrum estimation from quadratic measurements,” 2017 IEEE 7th Int. Work. Comput. Adv. Multi-Sensor Adapt. Process. CAMSAP 2017, vol. 2017-Decem, no. 2, pp. 1–4, 2018, doi: 10.1109/CAMSAP.2017.8313194.
  2. Z. Tian, Z. Zhang*, and Y. Wang, “Low-complexity optimization for two-dimensional direction-of-arrival estimation via decoupled atomic norm minimization,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2017, pp. 3071–3075. doi: 10.1109/ICASSP.2017.7952721.
2016
  1. 张柘, 张冰尘, 洪文, and 吴一戎, “结合MD自聚焦算法与回波模拟算子的快速稀疏微波成像误差补偿算法,” 雷达学报, vol. 5, no. 1, pp. 25–34, Feb. 2016, doi: 10.12000/JR15055.
  2. Z. Zhang, Z. Tian, B. Zhang, W. Hong, Y. Wu, and L. Li, “Multi-channel SAR covariance matrix estimation based on compressive covariance sensing,” in 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016, Sep. 2016, vol. 1, pp. 37–41. doi: 10.1109/CoSeRa.2016.7745695.
2015
  1. X. Quan, Z. Zhang, B. Zhang, W. Hong, and Y. Wu, “A study of BP-camp algorithm for SAR imaging,” in International Geoscience and Remote Sensing Symposium (IGARSS), Nov. 2015, vol. 2015-Novem, pp. 4480–4483. doi: 10.1109/IGARSS.2015.7326822.
  2. Z. Zhang, B. Zhang, W. Hong, H. Bi, and Y. Wu, “SAR imaging of moving target in a sparse scene based on sparse constraints: Preliminary experiment results,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2015, vol. 2015-Novem, pp. 2844–2847. doi: 10.1109/IGARSS.2015.7326407.
  3. B. C. Zhang, Z. Zhang*, C. L. Jiang, Y. Zhao, W. Hong, and Y. R. Wu, “System design and first airborne experiment of sparse microwave imaging radar: initial results,” Sci. China Inf. Sci., vol. 58, no. 6, pp. 1–10, 2015, doi: 10.1007/s11432-014-5266-6.
  4. 蒋成龙, 赵曜, 张柘, 张冰尘, and 洪文, “基于相关系数的稀疏微波成像方位向采样优化方法,” 电子与信息学报, vol. 37, no. 3, pp. 580–586, 2015, doi: 10.11999/JEIT140613.
  5. C. Jiang, Y. Lin, Z. Zhang, B. Zhang, and W. Hong, “WASAR imaging based on message passing with structured sparse constraint: Approach and experiment,” in 2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing, CoSeRa 2015, 2015, pp. 219–223. doi: 10.1109/CoSeRa.2015.7330296.
2014
  1. W. Wang, B. Zhang, C. Jiang, H. Bi, Z. Zhang, Y. Zhao, and W. Hong, “Polarimetric SAR tomography of forested areas based on compressive MUSIC,” Int. Geosci. Remote Sens. Symp., pp. 1875–1878, Nov. 2014, doi: 10.1109/IGARSS.2014.6946822.
  2. 吴一戎, 洪文, 张冰尘, 蒋成龙, 张柘, and 赵曜, “稀疏微波成像研究进展(科普类),” 雷达学报, vol. 3, no. 4, pp. 383–395, 2014, doi: 10.3724/SP.J.1300.2014.14105.
  3. C. Jiang, B. Zhang, J. Fang, Z. Zhang, W. Hong, Y. Wu and Z. Xu, “Efficient ℓq regularisation algorithm with range.azimuth decoupled for SAR imaging,” Electron. Lett., vol. 50, no. 3, pp. 204–205, Jan. 2014, doi: 10.1049/el.2013.1989.
  4. W. Hong, B. Zhang, Z. Zhang*, C. Jiang, Y. Zhao, and Y. Wu, “Radar imaging with sparse constraint: Principle and initial experiment,” Proc. Eur. Conf. Synth. Aperture Radar, EUSAR, vol. Proceeding, pp. 1235–1238, 2014.
2013
  1. Z. Zhang, Y. Zhao, C. Jiang, B. Zhang, W. Hong, and Y. Wu, “Autofocus of sparse microwave imaging radar based on phase recovery,” 2013 IEEE Int. Conf. Signal Process. Commun. Comput. ICSPCC 2013, 2013, doi: 10.1109/ICSPCC.2013.6663989.
  2. Z. Zhang, Y. Zhao, C. Jiang, B. Zhang, W. Hong, and Y. Wu, “Initial Analysis of SNR / Sampling Rate Constraints in Compressive Sensing based Imaging Radar,” 2nd Work. Compressive Sensng Appl. to Radar (CoSeRa 2013), vol. 55, no. 8, p. 100190, 2013.
  3. Z. Zhang, Y. Zhao, B. Zhang, W. Hong, and Y. Wu, “SNR / Sampling Rate Constraints of Sparse Microwave Imaging: Analysis and Experiments,” in Signal Processing with Adaptive Sparse Structured Representations (SPARS) 2013, 2013, p. 1.
2012
  1. B. Zhang, Z. Zhang*, W. Hong, and Y. Wu, “Applications of Distributed Compressive Sensing in Multi-channel Synthetic Aperture Radar,” in Workshop on Compressive Sensng Applied to Radar (CoSeRa) 2012, 2012, no. May, pp. 1–4. Accessed: Mar. 21, 2013. [Online]. Available: http://workshops.fhr.fraunhofer.de/cosera/pdf/1046_t.pdf
  2. Z. Zhang, B. C. Zhang, W. Hong, and Y. R. Wu, “Waveform design for Lqregularization based radar imaging and an approach to radar imaging with non-moving platform,” in Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR, 2012, vol. 2012-April, no. 2, pp. 685–688. Accessed: Mar. 21, 2013. [Online]. Available: http://www.vde-verlag.de/proceedings-en/453404253.html
  3. B. Zhang, C. Jiang, Z. Zhang, J. Fang, Y. Zhao, W. Hong, Y. Wu, and Z. Xu, “Azimuth Ambiguity Suppression for SAR Imaging based on Group Sparse Reconstruction,” Work. Compressive Sensng Appl. to Radar 2012, no. 3, p. 100190, 2012.
  4. C. Jiang, B. Zhang, Z. Zhang, W. Hong, and Y. Wu, “Experimental results and analysis of sparse microwave imaging from spaceborne radar raw data,” Sci. China Inf. Sci., vol. 55, no. 8, pp. 1801–1815, 2012, doi: 10.1007/s11432-012-4634-3.
  5. Z. Zhang, B. C. Zhang, C. L. Jiang, Y. Xiang, W. Hong, and Y. R. Wu, “Influence factors of sparse microwave imaging radar system performance: Approaches to waveform design and platform motion analysis,” Sci. China Inf. Sci., vol. 55, no. 10, pp. 2301–2317, 2012, doi: 10.1007/s11432-012-4603-x.
2010
  1. 谢敏, 乔瑞萍, 潘志斌, 李东平, 乔阳, and 张柘, “一种改进的绝对误差不等式删除算法在DM642上的实现,” 微电子学与计算机, vol. 27, no. 4, pp. 182–185, 2010, doi: 10.19304/j.cnki.issn1000-7180.2010.04.046.

科研活动

   
科研项目
( 1 ) 中国科学院“BR计划”:稀疏信号处理与深度学习及其在微波成像中的应用, 主持, 部委级, 2021-01--2024-01
( 2 ) 结构信号的自适应高效感知及在微波成像中的应用研究, 主持, 市地级, 2021-07--2023-07
( 3 ) 合成孔径雷达微波视觉三维成像理论与应用基础研究, 参与, 国家级, 2020-01--2024-12
( 4 ) 微波三维成像的高效感知系统与技术的研发, 主持, 省级, 2021-12--2024-12
( 5 ) 稀疏信号处理及其在微波成像中的应用研究, 主持, 国家级, 2022-01--2024-12
( 6 ) 先进微波探测与信息处理, 参与, 国家级, 2010-01--2014-12
( 7 ) 稀疏微波的成像理论、体制和方法研究, 参与, 国家级, 2010-01--2014-12
参与会议
(1)无网格稀疏信号处理及其在微波成像中的应用   雷达学报学术讲堂──当稀疏信号处理技术遇见雷达研讨会   2021-12-05
(2)Embedded Micro Radar for Pedestrian Detection in Clutter   2020-04-27
(3) Low-complexity optimization for Two- Dimensional Direction-of-arrival Estimation via Decoupled Atomic Norm Minimizationg   2017-05-03
(4)SAR Imaging of Moving Target in a Sparse Scene Based on Sparse Constraints: Preliminary Experiment Result   2015-07-26
(5)Radar Imaging with Sparse Constraint: Principle and Initial Experiment,   2014-06-02

指导学生

现指导学生

王泽华  硕士研究生  085400-电子信息  

王骜巍  硕士研究生  081002-信号与信息处理  

龚福友  硕士研究生  085400-电子信息  

协助指导研究生

赵丹丹(博士,联合培养)
高四琳(博士)
邵明肖(博士)
王沐涵(博士)
施睿哲(博士)
蒋鹏宇(博士)
吴雨微(博士)
金芷伊(硕士)
李子雅(硕士)
王怡心(硕士)