Submit Manuscript
Peer Review
Editor Work
- Home
-
Articles & Issues
-
Data
- Dataset of Radar Detecting Sea
- SAR Dataset
- SARGroundObjectsTypes
- SARMV3D
- 3DRIED
- UWB-HA4D
- LLS-LFMCWR
- FAIR-CSAR
- FUSAR
- MSAR
- SDD-SAR
- DatasetinthePaper
- SpaceborneSAR3Dimaging
- DatasetintheCompetition
- Sea-land Segmentation
- SAR-Airport
- Hilly and mountainous farmland time-series SAR and ground quadrat dataset
-
Report
-
Course
-
About
-
Publish
- Editorial Board
- Chinese
| Citation: | ZHU Hangui, FENG Weike, FENG Cunqian, et al. Deep unfolding based space-time adaptive processing method for airborne radar[J]. Journal of Radars, 2022, 11(4): 676–691. doi: 10.12000/JR22051
|
Deep Unfolding Based Space-Time Adaptive Processing Method for Airborne Radar
DOI: 10.12000/JR22051 CSTR: 32380.14.JR22051
More Information-
Abstract
The Sparse Recovery Space-Time Adaptive Processing (SR-STAP) method can use a small number of training range cells to effectively suppress the clutter of airborne radar. The SR-STAP approach may successfully eliminate airborne radar clutter using a limited number of training range cells. However, present SR-STAP approaches are all model-driven, limiting their practical applicability due to parameter adjustment difficulties and high computational cost. To address these problems, this study, for the first time, introduces the Deep Unfolding/Unrolling (DU) method to airborne radar clutter reduction and target recognition by merging the model-driven SR method and the data-driven deep learning method. Firstly, a combined estimation model for clutter space-time spectrum and Array Error (AE) parameters is established and solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Secondly, the ADMM algorithm is unfolded to a deep neural network, named AE-ADMM-Net, to optimize all iteration parameters using a complete training dataset. Finally, the training range cell data is processed by the trained AE-ADMM-Net, jointly estimating the clutter space-time spectrum and the radar AE parameters efficiently and accurately. Simulation results show that the proposed DU-STAP method can achieve higher clutter suppression performance with lower computational cost compared to typical SR-STAP methods. -
References
[1] 谢文冲, 段克清, 王永良. 机载雷达空时自适应处理技术研究综述[J]. 雷达学报, 2017, 6(6): 575–586. doi: 10.12000/JR17073XIE Wenchong, DUAN Keqing, and WANG Yongliang. Space time adaptive processing technique for airborne radar: An overview of its development and prospects[J]. Journal of Radars, 2017, 6(6): 575–586. doi: 10.12000/JR17073[2] BRENNAN L E and REED L S. Theory of adaptive radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1973, AES-9(2): 237–252. doi: 10.1109/TAES.1973.309792[3] YANG Zhaocheng, WANG Zetao, LIU Weijian, et al. Reduced-dimension space-time adaptive processing with sparse constraints on beam-Doppler selection[J]. Signal Processing, 2019, 157: 78–87. doi: 10.1016/j.sigpro.2018.11.013[4] PECKHAM C D, HAIMOVICH A M, AYOUB T F, et al. Reduced-rank STAP performance analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 664–676. doi: 10.1109/7.845257[5] SARKAR T K, WANG Hong, PARK S, et al. A deterministic least-squares approach to space-time adaptive processing (STAP)[J]. IEEE Transactions on Antennas and Propagation, 2001, 49(1): 91–103. doi: 10.1109/8.910535[6] WU Yong, TANG Jun, and PENG Yingning. On the essence of knowledge-aided clutter covariance estimate and its convergence[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(1): 569–585. doi: 10.1109/TAES.2011.5705692[7] 段克清, 袁华东, 许红, 等. 稀疏恢复空时自适应处理技术研究综述[J]. 电子学报, 2019, 47(3): 748–756. doi: 10.3969/j.issn.0372-2112.2019.03.033DUAN Keqing, YUAN Huadong, XU Hong, et al. An overview on sparse recovery space-time adaptive processing technique[J]. Acta Electronica Sinica, 2019, 47(3): 748–756. doi: 10.3969/j.issn.0372-2112.2019.03.033[8] JIANG Zhizhuo, WANG Xueqian, LI Gang, et al. Space-time adaptive processing by employing structure-aware two-level block sparsity[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 6386–6397. doi: 10.1109/JSTARS.2021.3090069[9] 孙珂, 张颢, 李刚, 等. 基于杂波谱稀疏恢复的空时自适应处理[J]. 电子学报, 2011, 39(6): 1389–1393.SUN Ke, ZHANG Hao, LI Gang, et al. STAP via sparse recovery of clutter spectrum[J]. Acta Electronica Sinica, 2011, 39(6): 1389–1393.[10] SUN Ke, MENG Huadong, WANG Yongliang, et al. Direct data domain STAP using sparse representation of clutter spectrum[J]. Signal Processing, 2011, 91(9): 2222–2236. doi: 10.1016/j.sigpro.2011.04.006[11] YANG Zhaocheng, LI Xiang, WANG Hongqiang, et al. On clutter sparsity analysis in space-time adaptive processing airborne radar[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1214–1218. doi: 10.1109/LGRS.2012.2236639[12] YANG Zhaocheng, LI Xiang, WANG Hongqiang, et al. Adaptive clutter suppression based on iterative adaptive approach for airborne radar[J]. Signal Processing, 2013, 93(12): 3567–3577. doi: 10.1016/j.sigpro.2013.03.033[13] DUAN Keqing, WANG Zetao, XIE Wenchong, et al. Sparsity-based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar[J]. IET Signal Processing, 2017, 11(5): 544–553. doi: 10.1049/iet-spr.2016.0183[14] WANG Zetao, WANG Yongliang, DUAN Keqing, et al. Subspace-augmented clutter suppression technique for STAP radar[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 462–466. doi: 10.1109/LGRS.2016.2519765[15] FENG Weike, GUO Yiduo, ZHANG Yongshun, et al. Airborne radar space time adaptive processing based on atomic norm minimization[J]. Signal Processing, 2018, 148: 31–40. doi: 10.1016/j.sigpro.2018.02.008[16] GUO Yiduo, LIAO Guisheng, and FENG Weike. Sparse representation based algorithm for airborne radar in beam-space post-Doppler reduced-dimension space-time adaptive processing[J]. IEEE Access, 2017, 5: 5896–5903. doi: 10.1109/ACCESS.2017.2689325[17] MA Zeqiang, LIU Yimin, MENG Huadong, et al. Sparse recovery-based space-time adaptive processing with array error self-calibration[J]. Electronics Letters, 2014, 50(13): 952–954. doi: 10.1049/el.2014.0315[18] YANG Zhaocheng, DE LAMARE R C, and LIU Weijian. Sparsity-based STAP using alternating direction method with gain/phase errors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(6): 2756–2768. doi: 10.1109/TAES.2017.2714938[19] GREGOR K and LECUN Y. Learning fast approximations of sparse coding[C]. The 27th International Conference on International Conference on Machine Learning, Haifa, Israel, 2010: 399–406.[20] LIU Jialin, CHEN Xiaohan, WANG Zhangyang, et al. ALISTA: Analytic weights are as good as learned weights in LISTA[C]. 7th International Conference on Learning Representations (ICLR), New Orleans, USA, 2019.[21] BORGERDING M, SCHNITER P, and RANGAN S. AMP-inspired deep networks for sparse linear inverse problems[J]. IEEE Transactions on Signal Processing, 2017, 65(16): 4293–4308. doi: 10.1109/TSP.2017.2708040[22] YANG Chengzhu, GU Yuantao, CHEN Badong, et al. Learning proximal operator methods for nonconvex sparse recovery with theoretical guarantee[J]. IEEE Transactions on Signal Processing, 2020, 68: 5244–5259. doi: 10.1109/TSP.2020.2978615[23] 朱晗归, 冯存前, 冯为可, 等. 一种深度学习稀疏单快拍DOA估计方法[J/OL]. 信号处理. https://kns.cnki.net/KCMS/detail/11.2406.TN.20220130.1421.006.ktml, 2022.ZHU Hangui, FENG Cunqian, FENG Weike, et al. A deep learning approach for sparse single snapshot DOA estimation[J/OL]. Journal of Signal Processing. https://kns.cnki.net/KCMS/detail/11.2406.TN.20220130.1421.006.ktml, 2022.[24] GUO Yiduo, LIAO Guisheng, GONG Jian, et al. Sparse recovery-based STAP method using prior information of azimuth-elevation[J]. Journal of Applied Remote Sensing, 2017, 11(3): 035004. doi: 10.1117/1.JRS.11.035004[25] 段克清, 李想, 行坤, 等. 基于卷积神经网络的天基预警雷达杂波抑制方法[J]. 雷达学报, 2022, 11(3): 386–398. doi: 10.12000/JR21161DUAN Keqing, LI Xiang, XING Kun, et al. Clutter mitigation in space-based early warning radar using a convolutional neural network[J]. Journal of Radars, 2022, 11(3): 386–398. doi: 10.12000/JR21161[26] Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine learning, 2011, 3(1): 1–122.[27] YANG Junpeng and ZHANG Yin. Alternating direction algorithms for l1-problems in compressive sensing[J]. SIAM Journal on Scientific Computing, 2011, 33(1): 250–278. doi: 10.1137/090777761[28] RUMELHART D E, HINTON G E, and WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533–536. doi: 10.1038/323533a0[29] HU Xiaowei, XU Feng, GUO Yiduo, et al. MDLI-Net: Model-driven learning imaging network for high-resolution microwave imaging with large rotating angle and sparse sampling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5212617. doi: 10.1109/TGRS.2021.3110579 Relative Articles
[1] HU Xueyao, LIANG Can, LU Shanshan, WANG Zaiyang, ZHENG Le, LI Yang. Matrix Completion-based Range-Doppler Spectrum Estimation for Random Stepped-frequency Radars[J]. Journal of Radars, 2024, 13(1): 200-214. doi: 10.12000/JR23176 [2] WANG Xiang, WANG Yumiao, CHEN Xingyu, ZANG Chuanfei, CUI Guolong. Deep Learning-based Marine Target Detection Method with Multiple Feature Fusion[J]. Journal of Radars, 2024, 13(3): 554-564. doi: 10.12000/JR23105 [3] LIAO Zhipeng, DUAN Keqing, HE Jinjun, QIU Zizhou, WANG Yongliang. Interpretable STAP Algorithm Based on Deep Convolutional Neural Network[J]. Journal of Radars, 2024, 13(4): 917-928. doi: 10.12000/JR24024 [4] ZHANG Yushi, LI Xiaoyu, ZHANG Jinpeng, XIA Xiaoyun. Sea Clutter Spectral Parameters Prediction and Influence Factor Analysis Based on Deep Learning[J]. Journal of Radars, 2023, 12(1): 110-119. doi: 10.12000/JR22133 [5] CHEN Xiang, WANG Liandong, XU Xiong, SHEN Xujian, FENG Yuntian. A Review of Radio Frequency Fingerprinting Methods Based on Raw I/Q and Deep Learning[J]. Journal of Radars, 2023, 12(1): 214-234. doi: 10.12000/JR22140 [6] TIAN Ye, DING Chibiao, ZHANG Fubo, SHI Min’an. SAR Building Area Layover Detection Based on Deep Learning[J]. Journal of Radars, 2023, 12(2): 441-455. doi: 10.12000/JR23033 [7] HE Mi, PING Qinwen, DAI Ran. Fall Detection Based on Deep Learning Fusing Ultrawideband Radar Spectrograms[J]. Journal of Radars, 2023, 12(2): 343-355. doi: 10.12000/JR22169 [8] WANG Mou, WEI Shunjun, SHEN Rong, ZHOU Zichen, SHI Jun, ZHANG Xiaoling. 3D SAR Imaging Method Based on Learned Sparse Prior[J]. Journal of Radars, 2023, 12(1): 36-52. doi: 10.12000/JR22101 [9] DING Zihang, XIE Junwei, WANG Bo. Missing Covariance Matrix Recovery with the FDA-MIMO Radar Using Deep Learning Method[J]. Journal of Radars, 2023, 12(5): 1112-1124. doi: 10.12000/JR23002 [10] HUANG Zhongling, YAO Xiwen, HAN Junwei. Progress and Perspective on Physically Explainable Deep Learning for Synthetic Aperture Radar Image Interpretation（in English）[J]. Journal of Radars, 2022, 11(1): 107-125. doi: 10.12000/JR21165 [11] QU Haiyou, CHENG Di, CHEN Chang, CHEN Weidong. High-resolution Sparse Self-calibration Imaging for Vortex Radar with Phase Error[J]. Journal of Radars, 2021, 10(5): 699-717. doi: 10.12000/JR21094 [12] CUI Ning, XING Kun, DUAN Keqing, YU Zhongjun. Fast Tensor-based Three-dimensional Sparse Bayesian Learning Space-Time Adaptive Processing Method[J]. Journal of Radars, 2021, 10(6): 919-928. doi: 10.12000/JR21140 [13] ZHANG Jinsong, XING Mengdao, SUN Guangcai. A Water Segmentation Algorithm for SAR Image Based on Dense Depthwise Separable Convolution[J]. Journal of Radars, 2019, 8(3): 400-412. doi: 10.12000/JR19008 [14] Zhao Feixiang, Liu Yongxiang, Huo Kai. A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine[J]. Journal of Radars, 2018, 7(5): 613-621. doi: 10.12000/JR18048 [15] Wang Jun, Zheng Tong, Lei Peng, Wei Shaoming. Study on Deep Learning in Radar[J]. Journal of Radars, 2018, 7(4): 395-411. doi: 10.12000/JR18040 [16] Xie Wenchong, Duan Keqing, Wang Yongliang. Space Time Adaptive Processing Technique for Airborne Radar: An Overview of Its Development and Prospects[J]. Journal of Radars, 2017, 6(6): 575-586. doi: 10.12000/JR17073 [17] Xu Feng, Wang Haipeng, Jin Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification[J]. Journal of Radars, 2017, 6(2): 136-148. doi: 10.12000/JR16130 [18] Ma Ze-qiang, Wang Xi-qin, Liu Yi-min, Meng Hua-dong. An Overview on Sparse Recovery-based STAP[J]. Journal of Radars, 2014, 3(2): 217-228. doi: 10.3724/SP.J.1300.2014.14002 [19] Duan Ke-qing, Wang Ze-tao, Xie Wen-chong, Gao Fei, Wang Yong-liang. A Space-time Adaptive Processing Algorithm Based on Joint Sparse Recovery[J]. Journal of Radars, 2014, 3(2): 229-234. doi: 10.3724/SP.J.1300.2014.13149 [20] Tian Jing, Liao Gui-sheng, Yang Zhi-wei. Performance Analysis of Oblique Projection Filtering Based on Polarization Sensitive Array[J]. Journal of Radars, 2013, 2(3): 284-291. doi: 10.3724/SP.J.1300.2013.13043 Cited by
Periodical cited type(5)
1. 廖志鹏，段克清，何锦浚，邱梓洲，王永良. 基于可解释深度卷积网络的空时自适应处理方法. 雷达学报. 2024(04): 917-928 . 
本站查看2. 李海，张强，周桉宇，熊玉. 卷积神经网络STAP低空风切变风速估计. 电子与信息学报. 2024(08): 3193-3201 . 
3. 毛德庆，杨建宇，杨明杰，张永超，张寅，黄钰林. IAA-Net：一种实孔径扫描雷达迭代自适应角超分辨成像方法. 雷达学报. 2024(05): 1073-1091 . 本站查看4. 钟永磊. 海杂波对机载雷达探测距离的影响. 无线互联科技. 2023(11): 5-7 . 5. 邹帛，王欣，冯为可，朱晗归，李瑶. 基于非线性回归的杂波重构STAP方法. 电光与控制. 2022(09): 32-37 . Other cited types(2)
-
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- Conference
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-8
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Gu Fufei, Zhang Qun, Yang Qiu, Huo Wenjun, Wang Min. Compressed Sensing Imaging Algorithm for High-squint SAR Based on NCS Operator[J]. Journal of Radars, 2016, 5(1): 16-24. doi: 10.12000/JR15035
Format
Content
DownLoad:
- Figure 1. Geometry model of airborne radar
- Figure 2. The data flow graph of ADMM algorithm
- Figure 3. The network structure of AE-ADMM-Net
- Figure 4. Four sub-layers of AE-ADMM-Net
- Figure 5. Clutter space-time spectra and array error parameters estimated via ADMM algorithm with fixed parameters (a1—a4: Clutter space-time spectra estimation results in different array error parameters, b1—b4: Amplitude error estimation results in different array error parameters, c1—c4: Phase error estimation results in different array error parameters)
- Figure 6. Convergence performance of AE-ADMM-Net and its comparison with ADMM algorithm
- Figure 7. Clutter space-time spectra estimated via different algorithms under different conditions (a1—a4: estimation results of ADMM algorithm with 25 iterations in different array error parameters, b1—b4: estimation results of ADMM algorithm with 45 iterations in different array error parameters, c1—c4: estimation results of FOCUSS algorithm with 200 iterations in different array error parameters, d1—d4: estimation results of SBL algorithm with 400 iterations in different array error parameters, e1—e4: estimation results of AE-ADMM-Net with 25 layers in different array error parameters, f1—f4: estimation results of AE-ADMM-Net with 45 layers in different array error parameters)
- Figure 8. Array error parameters estimated by AE-ADMM-Net under different conditions (a1—a4: Amplitude error estimation results in different array error parameters, b1—b4: Phase error estimation results in different array error parameters)
- Figure 9. SCNR loss curves corresponding to different methods under different conditions (a1—a4: SCNR loss curves results in different array error parameters, b1—b4: SCNR loss curves results with enlarged scale in different array error parameters)
- Figure 10. Running time of different algorithms under different conditions
- Figure 11. Processing results of Mountain Top actual measured data