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

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. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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
11. 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.
    
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.
27. 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.
    
28. 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.
29. 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.
30. 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
31. 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.
32. 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.
33. 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.
34. 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.
35. 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.
36. 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.
37. 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.
38. 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.
39. 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.
40. 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.
41. 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.
42. 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.
43. 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.
44. 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.
45. 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.
46. 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.
47. 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.
48. 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.
49. 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.
50. 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.
51. 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.
52. 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.
53. 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.
54. 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
55. 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
56. 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.
57. 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.
58. 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.
59. 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.

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  

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  

Dandan Zhao 赵丹丹 (PhD, Joint with Hainan University)
Silin Gao 高四琳 (PhD)
Mingxiao Shao 邵明肖 (PhD)
Muhan Wang 王沐涵 (PhD)
Ruizhe Shi 施睿哲 (PhD)
Pengyu Jiang 蒋鹏宇 (PhD)
Yuwei Wu 吴雨微 (PhD)
Zhiyi Jin 金芷伊 (Master)
Ziya Li 李子雅 (Master)
Yixin Wang 王怡心 (Master)

Prize

National Disruptive Technology Innovation Contest 2021, Final Winner Prize