Sparse signal processing：Convex optimization; Sparse representation; Atomic norm minimization; etc.
Sparse microwave imaging：Application of sparse signal processing in two-dimensional and three-dimensional microwave imaging; New generation synthetic aperture radar; etc.
Combination of signal processing and deep learning：Neural network interpretability; Hybrid neural network; Application of signal processing in neural networks; etc.

​2014-01--2014-04   University of Connecticut                                                   Visiting student
2009-09--2015-07   Institute of Electronics, Chinese Academy of Sciences      PhD in Signal and Information Processing
2004-09--2008-07   Xi'an Jiaotong University                                                    Bachelor in Information Engineering
2003-09--2004-07   Xi'an Jiaotong University                                                    Special Class for Gifted Young

   

2025-09~present,   Aerospace Information Technology University, School of Electronics and Information, Dean, Professor
2023-08~present,   University of Chinese Academy of Sciences, Adjunct Professor
2023-01~present,   Aerospace Information Research Institute, Chinese Academy of Sciences, Suzhou Campus, Lab 22 Director Assistant / Academic Leader
2022-04~present,   Aerospace Information Research Institute, Chinese Academy of Sciences, Suzhou Campus, Professor
2021-01~2022-04,  Aerospace Information Research Institute, Chinese Academy of Sciences, Suzhou Campus, Associate Professor
2016-12~2020-06,  George Mason University, Postdoctoral Research Fellow
2015-12~2016-11,  George Washington University, Postdoctoral Research Scientist

   

1. L. Peng*, D. Zhao, R. Hu, S. Niu, Z. Zhang, and X. Qiu, “SAR Image Translation Based on Complex-Valued Convolutional Neural Networks: From Single Polarization to Full Polarization,” in IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Aug. 2025, pp. 9342–9345. doi: 10.1109/igarss55030.2025.11242463.
2. 许文嘉, 于睿卿, 薛铭浩, 汪雪怡, 张源奔, 魏智威, 张柘, 彭木根, and 吴一戎, "多模态对地观测大模型：架构、关键技术和未来展望," 雷达学报, vol. 15, no. 1, pp. 361–366, 2026.
3. Z. Ma, X. Ye, Z. Zhang*, B. Zhang, and X. Qiu, “PTIR-Net: A Joint Optimization Network of Pulse Transmission and Sparse Reconstruction for Azimuth Multichannel SAR System,” IEEE Geosci. Remote Sens. Lett., vol. 23, pp. 1–5, 2026, doi: 10.1109/LGRS.2025.3645604.
4. Y. Huang, W. Tie*, Z. Zhang, H. Li, and D. Lu, “Hierarchical Framework for Remote Sensing Deep Learning Evaluation on Terminal Devices,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 18, pp. 28920–28938, 2025, doi: 10.1109/JSTARS.2025.3629641.
5. Y. Zhao, W. Yang, K. Han, W. K. Ling, Z. Pan, and Z. Zhang, “Sparse and Low-rank Decomposition of Hankel Matrix for SAR Imaging,” 2024 4th Int. Conf. Commun. Technol. Inf. Technol. ICCTIT 2024, pp. 730–733, 2024, doi: 10.1109/ICCTIT64404.2024.10928650.
6. Z. Ma, Z. Zhang*, B. Zhang, and X. Qiu, “PUSIF-DUNet: Deep SAR Imaging Network With Joint Optimization on Learnable Probabilistic Undersampling and Unambiguous Reconstruction,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 18, pp. 28217–28233, 2025, doi: 10.1109/JSTARS.2025.3625733.
7. H. Huang, W. Li, H. Li, Y. Zhu, D. Lu, and Z. Zhang, “SARRSD: large-scale microwave radar remote sensing mapping-type and recognition-type object detection dataset,” in Second Conference of Young Scientists of the Chinese Society of Optical Engineering, L. Cao, Q. Zhang, P. Hu, and L. Liu, Eds., SPIE, Sep. 2025, p. 105. doi: 10.1117/12.3073531.
8. Y. Zhao, K. Liu, D. Lu, and Z. Zhang, “A multimodal remote sensing image fusion method for object detection based on tensor decomposition,” in Second Conference of Young Scientists of the Chinese Society of Optical Engineering, L. Cao, Q. Zhang, P. Hu, and L. Liu, Eds., SPIE, Sep. 2025, p. 100. doi: 10.1117/12.3073498.
9. F. Gong, C. Wang, D. Lu, H. Li, and Z. Zhang*, “Research on precise positioning method of low-altitude targets based on DOA estimation,” in Second Conference of Young Scientists of the Chinese Society of Optical Engineering, L. Cao, Q. Zhang, P. Hu, and L. Liu, Eds., SPIE, Sep. 2025, p. 211. doi: 10.1117/12.3075320.
10. 王骜巍, 商明样, and 张柘*, “一种改进DBF方法用于多通道SAR非均匀采样重构,” 河南科技大学学报, vol. 46, no. 3, pp. 1–10, 2025.
11. 海那尔·哈那提, 张柘, 朱利鲁, and 吴一戎*, “基于区块链的多模态遥感大数据治理方法与系统,” 河南科技大学学报, vol. 46, no. 2, pp. 1–11, 2025, doi: 10.15926/j.cnki.issn1672-6871.2025.02.001.
12. C. Wang, Q. Yan, X. Qiu, Y. Luo, L. Peng, and Z. Zhang, “A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application,” J. Remote Sens., vol. 5, Jan. 2025, doi: 10.34133/remotesensing.0583.
13. Z. Jin, Z. Pan, Z. Zhang*, and X. Qiu, “SAAS-Net: Self-Supervised Sparse Synthetic Aperture Radar Imaging Network with Azimuth Ambiguity Suppression,” Remote Sens., vol. 17, no. 6, p. 1069, Mar. 2025, doi: 10.3390/rs17061069.
14. D. Zhao, Z. Zhang*, D. Lu, X. Qiu, W. Li, H. Li, and Y. Wu, “CV-YOLO: A Complex-Valued Convolutional Neural Network for Oriented Ship Detection in Single-Polarization Single-Look Complex SAR Images,” Remote Sens., vol. 17, no. 8, p. 1478, Apr. 2025, doi: 10.3390/rs17081478.
15. S. Song, X. Qiu*, S. Shangguan, Y. Luo, Z. Li, and Z. Zhang , “A polarization-power-maximum-based 3D imaging method for Ku-band UAV-borne fully-polarimetric array InSAR,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 18, pp. 10320–10336, 2025, doi: 10.1109/JSTARS.2025.3555662.
16. Z. Wang, Z. Wang, X. Qiu, and Z. Zhang*, “Fine classification of multi-frequency PolSAR images using an adaptive fusion network,” Remote Sens. Lett., vol. 16, no. 5, pp. 461–471, May 2025, doi: 10.1080/2150704X.2025.2470884.
17. Y. Huang, H. Li, D. Lu, Z. Zhang, and W. Tie*, “Intelligence Evaluating Computational Power: A Multi-Factor Method,” IEEE Access, vol. 13, pp. 27398–27415, 2025, doi: 10.1109/ACCESS.2025.3538977.
18. D. Wang, D. Lu, J. Zhao, W. Li, H. Li, J. Xu, J. Huang, and Z. Zhang*, “Multiscale Pillars Fusion for 4-D Radar Object Detection With Radar Data Enhancement,” IEEE Sens. J., vol. 25, no. 3, pp. 5102–5115, Feb. 2025, doi: 10.1109/JSEN.2024.3516786.
19. G. Zhou*, Y. Zuo, Z. Zhang, B. Zhang, and Y. Wu, “CR-DEQ-SAR: A Deep Equilibrium Sparse SAR Imaging Method for Compound Regularization,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 18, pp. 4680–4695, 2025, doi: 10.1109/JSTARS.2025.3533082.
20. R. Shi, Y. Luo, Z. Zhang, X. Qiu*, and C. Ding, “A Three-Dimensional Imaging Method for Unmanned Aerial Vehicle-Borne SAR Based on Nested Difference Co-Arrays and Azimuth Multi-Snapshots,” Remote Sens., vol. 17, no. 3, p. 516, Feb. 2025, doi: 10.3390/rs17030516.
21. M. Shao*, Y. Fan, Y. Zhang, Z. Zhang, J. Zhao, and B. Zhang, “A Novel Gridless Non-Uniform Linear Array Direction of Arrival Estimation Approach Based on the Improved Alternating Descent Conditional Gradient Algorithm for Automotive Radar System,” Remote Sens., vol. 17, no. 2, p. 303, Jan. 2025, doi: 10.3390/RS17020303.
22. P. Jiang, S. Gao, J. Zhao, Z. Zhang*, and B. Zhang, “Gridless DOA Estimation with Extended Array Aperture in Automotive Radar Applications,” Remote Sens., vol. 17, no. 1, p. 33, Dec. 2024, doi: 10.3390/rs17010033.
23. G. Zhou, Z. Zhang*, B. Zhang, and Y. Wu, “An innovative semantically guided SAR imaging and target enhancement method,” Electron. Lett., vol. 60, no. 24, p. e70123, Dec. 2024, doi: 10.1049/ell2.70123.
24. M. Wang, S. Gao, X. Qiu*, and Z. Zhang, “A Novel Phase Error Estimation Method for TomoSAR Imaging Based on Adaptive Momentum Optimizer and Joint Criterion,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 18, pp. 2042–2051, 2025, doi: 10.1109/JSTARS.2024.3506852.
25. X. Qiu*, Z. Jiao, Z. Zhang, Q. Yan, and C. Ding*, “Advances and prospects in SAR microwave vision three-dimensional imaging,” Natl. Sci. Open, vol. 3, no. 5, p. 20240009, Sep. 2024, doi: 10.1360/nso/20240009.
26. Y. Wu, Z. Zhang*, R. Song, X. Qiu, and W. Yu, “Azimuth Ambiguity Suppression for Sparse SAR Imaging Based on Unfolded Deep Network,” Proc. Eur. Conf. Synth. Aperture Radar, EUSAR, pp. 526–530, 2024.
27. H. Yang, Z. Huang*, and Z. Zhang, “Interpretable Attributed Scattering Center Extracted via Deep Unfolding,” in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Jul. 2024, pp. 2004–2008. doi: 10.1109/igarss53475.2024.10641709.
28. M. Wang, X. Qiu, S. Gao, and Z. Zhang, “LiDAR-to-SAR Point Cloud Segmentation via Unsupervised Domain Adaptation Network,” in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Jul. 2024, pp. 10808–10812. doi: 10.1109/igarss53475.2024.10641953.
29. A. Wang, M. Shang, X. Qiu, and Z. Zhang*, “L2 Regularized Reconstruction Matched Filter for Azimuth Multichannel SAR ,” in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Jul. 2024, pp. 1173–11176. doi: 10.1109/igarss53475.2024.10641476.
30. 仇晓兰, 罗一通, 宋舒洁, 彭凌霄, 程遥, 颜千程, 上官松涛, 焦泽坤, 张柘, 丁赤飚, “微波视觉三维SAR实验系统及其全极化数据处理方法,” 雷达学报, vol. 13, no. 5, pp. 941–954, Sep. 2024, doi: 10.12000/JR24137.
31. Y. Wu, R. Song, Z. Zhang*, X. Qiu, and W. Yu, “GSAT-Net: An Azimuth Ambiguity Suppression Network Based on Group Sparsity and Adaptive Threshold for Undersampling SAR Imaging,” IEEE Geosci. Remote Sens. Lett., vol. 21, pp. 1–5, 2024, doi: 10.1109/LGRS.2024.3452796.
32. M. Wang, X. Qiu*, Z. Zhang, and S. Gao, “A domain adaptation framework for cross-modality SAR 3D reconstruction point clouds segmentation utilizing LiDAR data,” Int. J. Appl. Earth Obs. Geoinf., vol. 133, p. 104103, Sep. 2024, doi: 10.1016/j.jag.2024.104103.
33. S. Gao, M. Wang, Z. Zhang*, B. Zhang, and Y. Wu, “Efficient Gridless DOA Estimation for Nonuniformly Spaced Linear Arrays in Automotive Radar Sensors,” IEEE Sens. J., vol. 24, no. 17, pp. 27737–27749, Sep. 2024, doi: 10.1109/JSEN.2024.3428530.
34. Y. Zhao, D. Xiao, Z. Pan, B. W. K. Ling, Y. Tian, and Z. Zhang*, “Sparse SAR Imaging Based on Non-Local Asymmetric Pixel-Shuffle Blind Spot Network,” Remote Sens., vol. 16, no. 13, p. 2367, Jun. 2024, doi: 10.3390/rs16132367.
35. Y. Wu, Z. Zhang*, X. Qiu, Y. Zhao, W. Yu, and R. Song, “An efficient azimuth sampling design network for sparse SAR imaging,” in IET International Radar Conference (IRC 2023), Institution of Engineering and Technology, 2023, pp. 2994–2998. doi: 10.1049/icp.2024.1570.
36. G. Zhou, Z. Xu, Y. Fan, Z. Zhang, B. Zhang*, and Y. Wu, “Deep unfolding network for sparse SAR imaging based on compound regularization,” in IET International Radar Conference (IRC 2023), Institution of Engineering and Technology, 2023, pp. 3536–3540. doi: 10.1049/icp.2024.1673.
37. Z. Wang, Z. Wang, X. Qiu, and Z. Zhang*, “End-to-end global segmentation of PolSAR images with data augmentation,” in IET International Radar Conference (IRC 2023), Institution of Engineering and Technology, 2023, pp. 1816–1820. doi: 10.1049/icp.2024.1359.
38. M. Wang, S. Gao, Z. Zhang, and X. Qiu*, “An autofocus network for multi-channel phase errors with application to tomoSAR imaging,” in IET International Radar Conference (IRC 2023), Institution of Engineering and Technology, 2023, pp. 3045–3050. doi: 10.1049/icp.2024.1580.
39. S. Gao, M. Wang, Z. Zhang*, B. Zhang, and Y. Wu, “Gridless DOA estimation for automotive radars with various array geometries: the non-Vandermonde atomic soft thresholding approach,” in IET International Radar Conference (IRC 2023), Institution of Engineering and Technology, 2023, pp. 2068–2073. doi: 10.1049/icp.2024.1406.
40. M. Shao, C. Su, Z. Zhang*, and B. Zhang, “The application of the alternate descent conditional gradient method in tomographic SAR off-grid imaging,” in IET International Radar Conference (IRC 2023), Institution of Engineering and Technology, 2023, pp. 3259–3264. doi: 10.1049/icp.2024.1622.
41. S. Gao, W. Wang, M. Wang, Z. Zhang*, Z. Yang, X. Qiu, B. Zhang, and Y. Wu, “A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography,” IEEE Trans. Geosci. Remote Sens., vol. 62, pp. 1–17, 2024, doi: 10.1109/TGRS.2024.3393972.
42. Y. Wu, Z. Zhang*, X. Qiu, Y. Zhao, and W. Yu, “MF-JMoDL-Net: A Sparse SAR Imaging Network for Undersampling Pattern Design towards Suppressed Azimuth Ambiguity,” IEEE Trans. Geosci. Remote Sens., vol. 62, pp. 1–18, 2024, doi: 10.1109/TGRS.2024.3397826.
43. S. Gao, M. Wang, Z. Zhang*, B. Zhang, and Y. Wu, “Efficient gridless 2D DOA estimation based on generalized matrix‐form atomic norm minimization,” Electron. Lett., vol. 60, no. 10, p. e13212, May 2024, doi: 10.1049/ell2.13212.
44. Y. Zhao, C. Ou, H. Tian, B. W.-K. Ling, Y. Tian, and Z. Zhang*, “Sparse SAR Imaging Algorithm in Marine Environments Based on Memory-Augmented Deep Unfolding Network,” Remote Sens., vol. 16, no. 7, p. 1289, Apr. 2024, doi: 10.3390/rs16071289.
45. Z. Wang, Z. Wang, X. Qiu, and Z. Zhang*, “Global Polarimetric Synthetic Aperture Radar Image Segmentation with Data Augmentation and Hybrid Architecture Model,” Remote Sens., vol. 16, no. 2, p. 380, Jan. 2024, doi: 10.3390/rs16020380.
46. Y. Zhao, Q. Liu, H. Tian, B. W.-K. Ling, and Z. Zhang*, “DeepRED Based Sparse SAR Imaging,” Remote Sens., vol. 16, no. 2, p. 212, Jan. 2024, doi: 10.3390/rs16020212.
47. M. Shao, Z. Zhang*, J. Li, J. Kang, and B. Zhang, “TADCG: A Novel Gridless Tomographic SAR Imaging Approach Based on the Alternate Descent Conditional Gradient Algorithm With Robustness and Efficiency,” IEEE Trans. Geosci. Remote Sens., vol. 62, pp. 1–13, 2024, doi: 10.1109/TGRS.2023.3345454.
48. Y. Bai, J. Kang*, X. Ding, A. Zhang, Z. Zhang, and N. Yokoya, “LaMIE: Large-Dimensional Multipass InSAR Phase Estimation for Distributed Scatterers,” IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1–15, Nov. 2023, doi: 10.1109/TGRS.2023.3330971.
49. 康健*, 童风雨, 白雨松, 丁翔, 冀腾宇, and 张柘*, “基于对数域加性信号分解的时序SAR图像相干斑抑制方法,” 雷达学报, vol. 12, no. 5, pp. 1031–1043, Mar. 2023, doi: 10.12000/JR22242.
50. T. Chen, Y. Meng, G. Zhou, Z. Zhang, B. Zhang, and Y. Wu, “An Improved Imaging Method for Highly-Squinted SAR Based on Hyper-Optimized ADMM,” in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Jul. 2023, pp. 4548–4551. doi: 10.1109/IGARSS52108.2023.10281842.
51. M. Wang, S. Gao, Z. Zhang*, and X. Qiu, “A Novel Multi-Channel Phase Error Estimation Method Based On Stochastic Optimization For Tomographic SAR Autofocusing,” in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Jul. 2023, pp. 7953–7956. doi: 10.1109/IGARSS52108.2023.10282948.
52. D. Zhao, Z. Zhang*, D. Lu, J. Kang, X. Qiu, and Y. Wu, “CVGG-Net: Ship Recognition for SAR Images Based on Complex-Valued Convolutional Neural Network,” IEEE Geosci. Remote Sens. Lett., vol. 20, pp. 1–5, 2023, doi: 10.1109/LGRS.2023.3316133.
53. Y. Zhao, Q. Liu, H. Tian, M. Luo, B. W.-K. Ling, and Z. Zhang*, “New convex approaches to general MVDR robust adaptive beamforming problems,” Electron. Lett., vol. 59, no. 18, p. e12957, Sep. 2023, doi: 10.1049/ell2.12957
54. G. Zhou, Z. Xu, Y. Fan, Z. Zhang, X. Qiu, B. Zhang, K. Fu* and Y. Wu, “HPHR-SAR-Net: Hyper-pixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 16, pp. 8595–8608, 2023, doi: 10.1109/JSTARS.2023.3295728.
55. Y. Zhao, Y. Chen, H. Tian, X. Quan, B. W.-K. Ling, and Z. Zhang*, “Wide angle SAR imaging method based on hybrid representation,” Electron. Lett., vol. 59, no. 15, p. e12897, Aug. 2023, doi: 10.1049/ell2.12897.
56. P. Jiang, Z. Zhang*, B. Zhang, and Z. Xu, “A novel TomoSAR imaging method with few observations based on nested array,” IET Radar, Sonar Navig., vol. 17, no. 6, pp. 925–938, Jun. 2023, doi: 10.1049/rsn2.12388.
57. R. Shi, Z. Zhang*, X. Qiu, and C. Ding, “A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With Applications in Tomographic SAR Imaging,” IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1–13, 2023, doi: 10.1109/TGRS.2023.3273568.
58. 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., vol. 61, pp. 1–18, 2023, doi: 10.1109/TGRS.2023.3268132.
59. 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., vol. 16, pp. 3784–3796, Jul. 2023, doi: 10.1109/JSTARS.2023.3263964.
60. J. Kang*, T. Ji, Z. Zhang, and R. Fernandez-Beltran, “SAR Time Series Despeckling via Nonlocal Matrix Decomposition in Logarithm Domain,” Signal Processing, vol. 209, p. 109040, Aug. 2023, doi: 10.1016/j.sigpro.2023.109040.
61. 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.
62. 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.
63. 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), Dec. 2022, pp. 34–40. doi: 10.1109/EEET58130.2022.00014.
64. 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 (RADAR 2022), 2023, pp. 48–53. doi: 10.1049/icp.2022.2290.
65. 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 (RADAR 2022), 2022, pp. 528–533. doi: 10.1049/icp.2023.1289.
66. 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.
67. 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.
68. 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.
69. 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.
70. 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.
71. 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.
72. 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.
73. 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
74. 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.
75. 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.
76. 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.
77. 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.
78. 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.
79. 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.
80. 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.
81. 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.
82. 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.
83. 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.
84. 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: 0.1109/CoSeRa.2016.7745695.
85. 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.
86. 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.
87. 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.
88. 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.
89. 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.
90. 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.
91. 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.
92. 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.
93. 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.
94. 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.
95. 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.
96. 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.
97. 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
98. 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
99. 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.
100. 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.
101. 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.
102. 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.

1. 上官松涛, 张柘, and 仇晓兰, “一种基于城区广泛强散射点参考的SAR辐射交叉定标方法,” CN202511185894.0.
2. 周欣, 邢朕诏, 张柘, 蔺蓓, 彭凌霄, and 金燕, “一种利用时间序列光学和SAR数据的冰川表面积雪识别方法”, CN202511027857.7.
3. 商明样, 王骜巍, 张柘, 王纯一, and 吕旖旎, “一种基于L2 正则的方位多通道SAR 非均匀采样重构方法及系统,” CN202510493241.2.
4. 张柘, 余佳童, 马梓瑞, 傅世平, 陈宇凡, 王纯一, and 吕旖旎, “一种基于深度展开网络的SAR 稀疏成像方法与系统,” CN202510486386.X.
5. 张柘, 吴雨微, 王纯一, 商明样, and 傅世平, “一种基于深度学习的SAR 方位向欠采样成像方法与系统,” CN202510471949.8.
6. 商明样, 顾杨钧, and 张柘, “一种面向超高分辨率星载SAR 图像的自适应量化方法及系统,” CN202510115690.3.
7. 马梓瑞, 张冰尘, 吕旖旎, and 张柘, ” 稀疏合成孔径雷达成像方法、装置、设备、介质及产品,” CN202510516768.2.
8. 铁雯婕, 卢东东, 李杭, 张柘, and 李翀, “一种面向运动模糊和雾霾影响的光学图像质量盲评价方法及系统,” CN202411939598.0.
9. 仇晓兰, 王沐涵, 高四琳, 罗一通, 张柘, and 焦泽坤, “一种基于微波视觉的阵列干涉SAR误差估计方法与装置,” ZL202411777220.5.
10. 施睿哲, 罗一通, 张柘, and 仇晓兰, “基于嵌套的三维SAR 天线阵列设计方法与应用,” CN202411175663.7.
11. 徐仲秋, 张冰尘, 蒋鹏宇, 张柘, and 吴一戎, “基于非凸-非局部全变差正则化的稀疏SAR成像方法及系统,” ZL202111402837.5.
12. 张柘, 赵曜, 张冰尘, 洪文, and 吴一戎, “一种基于相位恢复的机载稀疏微波成像自聚焦方法,” ZL201310737404.4.
13. 张柘, 张冰尘, 洪文, 吴一戎, and 全相印, “一种基于PhaseLift 的稀疏微波成像自聚焦方法,” ZL201510227896.1.
14. 张冰尘, 洪文, 吴一戎, and 张柘, “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,” ZL201510227896.1.
15. 全相印, 张冰尘, 蒋成龙, 赵曜, 张柘, and 吴一戎, “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,” ZL201410497525.0.
16. 吴一戎, 全相印, 张冰尘, and 张柘, “基于正则化的偏置相位中心天线成像方法,” ZL201610202747.4.

已指导学生

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  

现指导学生

余佳童  博士研究生  081002-信号与信息处理  

王栋  博士研究生  081002-信号与信息处理  

李上  博士研究生  081002-信号与信息处理  

秦晓灵  硕士研究生  081002-信号与信息处理  

胡朝阳  博士研究生  081002-信号与信息处理  

朱一星  博士研究生  081002-信号与信息处理  

赵韵棋  硕士研究生  085400-电子信息  

Zhiqi Huang 黄智棋 (PhD, Joint with Shanghai Jiao Tong University)

Dandan Zhao 赵丹丹 (PhD, Joint with Hainan University, Graduated)

Zhenzhao Xing 邢朕诏（PhD）

Junjie Wang 王俊杰 (PhD)

Silin Gao 高四琳 (PhD, Graduated)

Mingxiao Shao 邵明肖 (PhD, Graduated)

Muhan Wang 王沐涵 (PhD, Graduated)

Ruizhe Shi 施睿哲 (PhD, Graduated)

Pengyu Jiang 蒋鹏宇 (PhD, Graduated)

Yuwei Wu 吴雨微 (PhD, Graduated)

Zirui Ma 马梓睿 (PhD)

Zeyi Li 李则一(M.Sc.)

Prize & Honor

National Disruptive Technology Innovation Contest 2021, Final Winner Prize

Jiangsu Chief Science and Technology Communicating Expert