Volume 11 Issue 5
Oct.  2022
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2022  >  11(5): 871-883
Chen Xiao-long, Dong Yun-long, Li Xiu-you, Guan Jian. Modeling of Micromotion and Analysis of Properties of Rigid Marine Targets[J]. Journal of Radars, 2015, 4(6): 630-638. doi: 10.12000/JR15079
Citation: DUAN Keqing, LI Yufan, YANG Xingjia, et al. Reduced degrees of freedom in space-time adaptive processing for space-based early warning radar[J]. Journal of Radars, 2022, 11(5): 871–883. doi: 10.12000/JR22075
shu

Reduced Degrees of Freedom in Space-Time Adaptive Processing for Space-based Early Warning Radar

DOI: 10.12000/JR22075
  • 1.

    School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

  • 2.

    Air Force Early Warning Academy, Wuhan 430019, China

Funds:  The National Natural Science Foundation of China (61871397)
More Information
  • Corresponding author: WANG Yongliang, ylwangkjld@163.com
  • Received Date: 2022-04-26
  • Rev Recd Date: 2022-06-08
    • Available Online: 2022-06-15
  • Publish Date: 2022-07-13
    Abstract
  • The clutter of space-based early warning radar exhibits tight coupling in the azimuth-elevation-Doppler domain due to the high speed of satellites and the Earth’s rotation. As a result, conventional Space-Time Adaptive Processing (STAP) suffers significant performance degradation when detecting slow moving targets. The azimuth-elevation-Doppler three-dimensional STAP method provides the ability to decouple clutter and thus can achieve sub-optimal performance for clutter suppression. However, in contrast to the situation in non-sidelooking airborne early warning radar, this method requires large system degrees of freedom when applied to space-based early warning radar. Therefore, in practice, both the computational load and the sample requirement are too large to meet. In this study, the space-time signal model of the planar array for space-based early warning radar is first constructed. Then, the tight coupling characteristic of clutter in the azimuth-elevation-Doppler domain is analyzed in detail. On this basis, a novel three-dimensional STAP method with reduced degrees of freedom with factored structure is proposed. The sidelobe clutter is first suppressed via amplitude taper in azimuth, and the mainlobe clutter responding to each ambiguous range is further canceled by adaptive processing in the elevation-Doppler domain. The simulation results show that the proposed method can achieve sub-optimal performance under low computational load and limited sample conditions. Therefore, the proposed method is suitable for practical application in space-based early warning radar.

     

    • FullText(HTML)
    • References(27)
  • [1]
    DAVIS M E. Technology challenges in affordable space based radar[C]. Record of the IEEE 2000 International Radar Conference, Alexandria, USA, 2000: 18–23.
    [2]
    PILLAI S U, LI Keyong, and HIMED B. Space Based Radar: Theory & Applications[M]. McGraw Hill, 2008.
    [3]
    林幼权, 武楠. 天基预警雷达[M]. 北京: 国防工业出版社, 2017: 1–2.

    LIN Youquan and WU Nan. Space Based Early Warning Radar[M]. Beijing: National Defense Industry Press, 2017: 1–2.
    [4]
    HOVANESSIAN S A, JOCIC L B, and LOPEZ J M. Spaceborne radar design equations and concepts[C]. IEEE Aerospace Conference, Snowmass, USA, 1997: 125–136.
    [5]
    DAVIS M E. L-band SBR moving target detection in SAR-GMTI modes[C]. IEEE Aerospace Conference, Big Sky, USA, 2004: 2211–2219.
    [6]
    CROCI R, DELFINO A, and MARCHETTI F. Space based radar technology evolution[C]. The 6th European Radar Conference, Rome, Italy, 2009: 601–604.
    [7]
    LANE S A, MURPHEY T W, and ZATMAN M. Overview of the innovative space-based radar antenna technology program[J]. Journal of Spacecraft and Rockets, 2011, 48(1): 135–145. doi: 10.2514/1.50252
    [8]
    李青, 林幼权, 武楠. 美国天基预警雷达发展历程及现状分析[J]. 现代雷达, 2018, 40(1): 7–10. doi: 10.16592/j.cnki.1004-7859.2018.01.002

    LI Qing, LIN Youquan, and WU Nan. Analysis of development history and status for American space-base early warning radar[J]. Modern Radar, 2018, 40(1): 7–10. doi: 10.16592/j.cnki.1004-7859.2018.01.002
    [9]
    BRENNAN L E and REED I 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
    [10]
    MELVIN W L. A STAP overview[J]. IEEE Aerospace and Electronic Systems Magazine, 2004, 19(1): 19–35. doi: 10.1109/MAES.2004.1263229
    [11]
    SCHUMAN H K, LI P G, SZCZEPANSKI W, et al. Space-time adaptive processing for space based radar[C]. IEEE Aerospace Conference, Big Sky, USA, 2004: 1904–1910.
    [12]
    ZULCH P, DAVIS M, ADZIMA L, et al. The earth rotation effect on a LEO L-Band GMTI SBR and mitigation strategies[C]. IEEE Radar Conference, Philadelphia, USA, 2004: 27–32.
    [13]
    PILLAI S U, HIMED B, and LI Keyong. Effect of earth’s rotation and range foldover on space-based radar performance[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 917–932. doi: 10.1109/TAES.2006.248188
    [14]
    雷志勇, 于永, 郑志彬. 地球自转对天基雷达杂波特性影响分析[J]. 现代雷达, 2017, 39(7): 21–24. doi: 10.16592/j.cnki.1004-7859.2017.07.005

    LEI Zhiyong, YU Yong, and ZHENG Zhibin. Analysis of earth rotation effect for clutter characteristic of space-based radar[J]. Modern Radar, 2017, 39(7): 21–24. doi: 10.16592/j.cnki.1004-7859.2017.07.005
    [15]
    KOGON S M, RABIDEAU D J, and BARNES R M. Clutter mitigation techniques for space-based radar[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, USA, 1999: 2323–2326.
    [16]
    LI Keyong, MANGIAT S, ZULCH P, et al. Clutter impacts on space based radar GMTI: A global perspective[C]. IEEE Aerospace Conference, Big Sky, USA, 2007: 1–15.
    [17]
    ZULCH P A, HANCOCK R H, MORAN W, et al. Transmit waveform diversity for space based radar[C]. IEEE Aerospace Conference, Big Sky, USA, 2006: 10–16.
    [18]
    LAPIERRE F D, VERLY J G, and VAN DROOGENBROECK M. New solutions to the problem of range dependence in bistatic STAP radars[C]. IEEE Radar Conference, Huntsville, USA, 2003: 452–459.
    [19]
    郁文贤, 张增辉, 胡卫东. 基于频率非均匀采样杂波谱配准的天基雷达STAP方法[J]. 电子与信息学报, 2009, 31(2): 358–362. doi: 10.3724/SP.J.1146.2007.01244

    YU Wenxian, ZHANG Zenghui, and HU Weidong. STAP method for space based radar based on spectrum registration with non-uniformed frequency samples[J]. Journal of Electronics &Information Technology, 2009, 31(2): 358–362. doi: 10.3724/SP.J.1146.2007.01244
    [20]
    HALE T B, TEMPLE M A, and WICKS M C. Clutter suppression using elevation interferometry fused with space-time adaptive processing[J]. Electronics Letters, 2001, 37(12): 793–794. doi: 10.1049/el:20010494
    [21]
    CORBELL P M and HALE T B. 3-dimensional STAP performance analysis using the cross-spectral metric[C]. IEEE Radar Conference, Philadelphia, USA, 2004: 610–615.
    [22]
    DUAN Keqing, XU Hong, YUAN Huadong, et al. Reduced-DOF three-dimensional STAP via subarray synthesis for nonsidelooking planar array airborne radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(4): 3311–3325. doi: 10.1109/TAES.2019.2958174
    [23]
    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/JR21161

    DUAN 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
    [24]
    MELVIN W L and SHOWMAN G A. An approach to knowledge-aided covariance estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 1021–1042. doi: 10.1109/TAES.2006.248216
    [25]
    ROSEN P A and DAVIS M E. A joint space-borne radar technology demonstration mission for NASA and the air force[C]. IEEE Aerospace Conference, Big Sky, USA, 2003: 437–444.
    [26]
    CAPON J. High-resolution frequency-wavenumber spectrum analysis[J]. Proceedings of the IEEE, 1969, 57(8): 1408–1418. doi: 10.1109/PROC.1969.7278
    [27]
    FERTIG L B. Analytical expressions for space-time adaptive processing (STAP) performance[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 42–53. doi: 10.1109/TAES.2014.130676
    • Relative Articles

  • Relative Articles

    [1]HUANG Zhongling, WU Chong, YAO Xiwen, WANG Lipeng, HAN Junwei. Physically Explainable Intelligent Perception and Application of SAR Target Characteristics Based on Time-frequency Analysis[J]. Journal of Radars, 2024, 13(2): 331-344. doi: 10.12000/JR23191
    [2]GAO Zhiqi, SUN Shuchen, HUANG Pingping, QI Yaolong, XU Wei. Improved L1/2 Threshold Iterative High Resolution SAR Imaging Algorithm[J]. Journal of Radars, 2023, 12(5): 1044-1055. doi: 10.12000/JR22243
    [3]CHEN Hongmeng, YU Jizhou, ZHANG Wenjie, LI Yachao, LI Jun, CAI Liang, LU Yaobing. Probability Model-driven Airborne Bayesian Forward-looking Super-resolution Imaging for Multitarget Scenario[J]. Journal of Radars, 2023, 12(6): 1125-1137. doi: 10.12000/JR23080
    [4]DU Bang, QIU Xiaolan, ZHANG Zhe, LEI Bin, DING Chibiao. L1 Minimization with Perturbation for Off-grid Tomographic SAR Imaging[J]. Journal of Radars, 2022, 11(1): 62-70. doi: 10.12000/JR21093
    [5]ZHAO Yao, XU Juncong, QUAN Xiangyin, CUI Li, ZHANG Zhe. Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures[J]. Journal of Radars, 2022, 11(1): 52-61. doi: 10.12000/JR21210
    [6]LI Jianbing, WANG Xuesong. Review of Radar Characteristics and Sensing Technologies of Distributed Soft Target[J]. Journal of Radars, 2021, 10(1): 86-99. doi: 10.12000/JR20052
    [7]XIE Jinwei, LI Zhenfang, WANG Fan, WANG Zhibin. SAR Tomography Imaging for Buildings Using an Inconsistency Criterion for Amplitude and Phase[J]. Journal of Radars, 2020, 9(1): 154-165. doi: 10.12000/JR19062
    [8]PAN Jie, WANG Shuai, LI Daojing, LU Xiaochun. High-resolution Wide-swath SAR Moving Target Imaging Technology Based on Distributed Compressed Sensing[J]. Journal of Radars, 2020, 9(1): 166-173. doi: 10.12000/JR19060
    [9]Wei Shunjun, Tian Bokun, Zhang Xiaoling, Shi Jun. Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming[J]. Journal of Radars, 2018, 7(6): 664-675. doi: 10.12000/JR17103
    [10]Hu Jingqiu, Liu Falin, Zhou Chongbin, Li Bo, Wang Dongjin. CS-SAR Imaging Method Based on Inverse Omega-K Algorithm[J]. Journal of Radars, 2017, 6(1): 25-33. doi: 10.12000/JR16027
    [11]Ren Xiaozhen, Yang Ruliang. Four-dimensional SAR Imaging Algorithm Based on Iterative Reconstruction of Magnitude and Phase[J]. Journal of Radars, 2016, 5(1): 65-71. doi: 10.12000/JR15135
    [12]Chen Wenfeng, Li Shaodong, Yang Jun, Ma Xiaoyan. Multiple Measurement Vectors ISAR Imaging Algorithm Based on a Class of Linearized Bregman Iteration[J]. Journal of Radars, 2016, 5(4): 389-401. doi: 10.12000/JR16057
    [13]Chen Ying, Zhong Fei, Guo Shuxu. Blind Compressed Sensing Parameter Estimation of Non-cooperative Frequency Hopping Signal[J]. Journal of Radars, 2016, 5(5): 531-537. doi: 10.12000/JR15106
    [14]Zhong Jinrong, Wen Gongjian. Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)[J]. Journal of Radars, 2016, 5(1): 99-108. doi: 10.12000/JR15056
    [15]Li Liechen, Li Daojing, Huang Pingping. Airship Sparse Array Antenna Radar Real Aperture Imaging Based on Compressed Sensing and Sparsity in Transform Domain[J]. Journal of Radars, 2016, 5(1): 109-117. doi: 10.12000/JR14159
    [16]Yang Jun, Zhang Qun, Luo Ying, Deng Donghu. Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing[J]. Journal of Radars, 2016, 5(1): 90-98. doi: 10.12000/JR14107
    [17]Xiao Peng, Wu Youming, Yu Ze, Li Chunsheng. Azimuth Ambiguity Suppression in SAR Images Based on Compressive Sensing Recovery Algorithm[J]. Journal of Radars, 2016, 5(1): 35-41. doi: 10.12000/JR16004
    [18]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
    [19]Liao Ming-sheng, Wei Lian-huan, Wang Zi-yun, Timo Balz, Zhang Lu. Compressive Sensing in High-resolution 3D SAR Tomography of Urban Scenarios[J]. Journal of Radars, 2015, 4(2): 123-129. doi: 10.12000/JR15031
    [20]Zhong Li-hua, Hu Dong-hui, Ding Chi-biao, Zhang Wen-yi. ISAR Sparse Aperture Imaging Algorithm for Large Size Target[J]. Journal of Radars, 2012, 1(3): 292-300. doi: 10.3724/SP.J.1300.2012.20033
    • Supplements(0)
    • Cited By

  • Cited by

    Periodical cited type(9)

    1. 陈姣，刘毓，邓云开，金重阳，杨志航. 基于BiLSTM-CNN的地基SAR永久散射体选取. 国外电子测量技术. 2024(07): 24-32 .
    2. 白泽朝，王彦平，王振海，胡俊，李洋，林赟. 地基大视场SAR形变监测的非均匀大气相位校正方法. 雷达学报. 2023(01): 53-63 . 本站查看
    3. 徐甫，王政，李振洪，李永生. 复杂环境下的地基雷达大气改正方法. 武汉大学学报(信息科学版). 2023(12): 2069-2081 .
    4. 杜年春，王玉明，沈向前，谢翔. 滑坡灾害监测的圆弧合成孔径雷达大气相位校正方法. 测绘学报. 2022(10): 2139-2148 .
    5. 金重阳，刘毓，邓云开，田卫明，胡政权. 面向复杂大气扰动的GB-InSAR相位误差补偿方法. 信号处理. 2022(11): 2432-2442 .
    6. 汤进，刘毓，邓云开，胡政权，陈姣. 地基干涉合成孔径雷达的大气相位补偿研究. 测绘科学. 2022(11): 145-154 .
    7. 朱嘉鑫，邓云开，田卫明，胡程. 基于K-means算法的时序GB-InSAR图像PS实时选择方法. 信号处理. 2021(03): 349-357 .
    8. 朱嘉鑫，李永建，肖刚，余东洋，付振华，张志家，朱晨浩，戴颖超. 基于SAR图像配准的地基干涉雷达间断测量技术. 露天采矿技术. 2021(05): 46-50 .
    9. 吴昊，刘毓，邓云开，田卫明. 基于气象数据辅助的GB-InSAR大气相位补偿方法. 信号处理. 2021(08): 1496-1506 .

    Other cited types(15)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-04010203040
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 10.8 %FULLTEXT: 10.8 %META: 72.8 %META: 72.8 %PDF: 16.4 %PDF: 16.4 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 17.6 %其他: 17.6 %其他: 0.6 %其他: 0.6 %China: 0.3 %China: 0.3 %Germany: 0.1 %Germany: 0.1 %India: 0.1 %India: 0.1 %Reserved: 0.1 %Reserved: 0.1 %San Jose: 0.2 %San Jose: 0.2 %Seattle: 0.0 %Seattle: 0.0 %Ulu Bedok: 0.0 %Ulu Bedok: 0.0 %[]: 0.6 %[]: 0.6 %三明: 0.1 %三明: 0.1 %上海: 1.4 %上海: 1.4 %中卫: 0.1 %中卫: 0.1 %临沂: 0.1 %临沂: 0.1 %丹东: 0.0 %丹东: 0.0 %乌鲁木齐: 0.1 %乌鲁木齐: 0.1 %伊斯法罕: 0.2 %伊斯法罕: 0.2 %佛山: 0.1 %佛山: 0.1 %保定: 0.0 %保定: 0.0 %凤凰城: 0.0 %凤凰城: 0.0 %包头: 0.0 %包头: 0.0 %北京: 12.0 %北京: 12.0 %北京市: 0.0 %北京市: 0.0 %北海: 0.1 %北海: 0.1 %南京: 1.9 %南京: 1.9 %南充: 0.0 %南充: 0.0 %南昌: 0.0 %南昌: 0.0 %厦门: 0.0 %厦门: 0.0 %台北: 0.2 %台北: 0.2 %台湾省: 0.0 %台湾省: 0.0 %合肥: 0.2 %合肥: 0.2 %周口: 0.1 %周口: 0.1 %呼和浩特: 0.2 %呼和浩特: 0.2 %咸阳: 0.1 %咸阳: 0.1 %哈尔滨: 0.3 %哈尔滨: 0.3 %哈尔滨市: 0.0 %哈尔滨市: 0.0 %嘉兴: 0.1 %嘉兴: 0.1 %大同: 0.1 %大同: 0.1 %天津: 0.2 %天津: 0.2 %太原: 0.0 %太原: 0.0 %宁波: 0.1 %宁波: 0.1 %安康: 0.0 %安康: 0.0 %宝鸡: 0.2 %宝鸡: 0.2 %宣城: 0.2 %宣城: 0.2 %宿迁: 0.2 %宿迁: 0.2 %巴音郭楞: 0.0 %巴音郭楞: 0.0 %常州: 0.5 %常州: 0.5 %常德: 0.0 %常德: 0.0 %广安: 0.0 %广安: 0.0 %广州: 0.8 %广州: 0.8 %张家口: 0.8 %张家口: 0.8 %张家界: 0.1 %张家界: 0.1 %徐州: 0.1 %徐州: 0.1 %慕尼黑: 0.2 %慕尼黑: 0.2 %成都: 1.6 %成都: 1.6 %扬州: 0.1 %扬州: 0.1 %新乡: 0.3 %新乡: 0.3 %新加坡: 0.0 %新加坡: 0.0 %新泽西: 0.0 %新泽西: 0.0 %无锡: 0.0 %无锡: 0.0 %昆明: 0.5 %昆明: 0.5 %杭州: 1.6 %杭州: 1.6 %桂林: 0.0 %桂林: 0.0 %武威: 0.1 %武威: 0.1 %武汉: 0.6 %武汉: 0.6 %汉中: 0.1 %汉中: 0.1 %沈阳: 0.0 %沈阳: 0.0 %沧州: 0.1 %沧州: 0.1 %济南: 0.1 %济南: 0.1 %济宁: 0.0 %济宁: 0.0 %淮安: 0.1 %淮安: 0.1 %深圳: 0.3 %深圳: 0.3 %温州: 0.1 %温州: 0.1 %渭南: 0.0 %渭南: 0.0 %湖州: 0.0 %湖州: 0.0 %湘潭: 0.0 %湘潭: 0.0 %漯河: 0.2 %漯河: 0.2 %烟台: 0.1 %烟台: 0.1 %焦作: 0.0 %焦作: 0.0 %玉林: 0.2 %玉林: 0.2 %珠海: 0.1 %珠海: 0.1 %瓦赫兰: 0.2 %瓦赫兰: 0.2 %石家庄: 0.7 %石家庄: 0.7 %福州: 0.1 %福州: 0.1 %秦皇岛: 0.2 %秦皇岛: 0.2 %纽约: 0.1 %纽约: 0.1 %绍兴: 0.0 %绍兴: 0.0 %美国伊利诺斯芝加哥: 0.1 %美国伊利诺斯芝加哥: 0.1 %芒廷维尤: 15.4 %芒廷维尤: 15.4 %芝加哥: 0.4 %芝加哥: 0.4 %苏州: 0.1 %苏州: 0.1 %衢州: 0.1 %衢州: 0.1 %西宁: 29.0 %西宁: 29.0 %西安: 2.2 %西安: 2.2 %贵港: 0.1 %贵港: 0.1 %贵阳: 0.3 %贵阳: 0.3 %赤峰: 0.0 %赤峰: 0.0 %运城: 0.3 %运城: 0.3 %郑州: 1.5 %郑州: 1.5 %重庆: 0.5 %重庆: 0.5 %铁岭: 0.1 %铁岭: 0.1 %镇江: 0.2 %镇江: 0.2 %长沙: 0.7 %长沙: 0.7 %长沙市: 0.0 %长沙市: 0.0 %青岛: 0.2 %青岛: 0.2 %香港: 0.0 %香港: 0.0 %香港特别行政区: 0.1 %香港特别行政区: 0.1 %齐齐哈尔: 0.2 %齐齐哈尔: 0.2 %其他其他ChinaGermanyIndiaReservedSan JoseSeattleUlu Bedok[]三明上海中卫临沂丹东乌鲁木齐伊斯法罕佛山保定凤凰城包头北京北京市北海南京南充南昌厦门台北台湾省合肥周口呼和浩特咸阳哈尔滨哈尔滨市嘉兴大同天津太原宁波安康宝鸡宣城宿迁巴音郭楞常州常德广安广州张家口张家界徐州慕尼黑成都扬州新乡新加坡新泽西无锡昆明杭州桂林武威武汉汉中沈阳沧州济南济宁淮安深圳温州渭南湖州湘潭漯河烟台焦作玉林珠海瓦赫兰石家庄福州秦皇岛纽约绍兴美国伊利诺斯芝加哥芒廷维尤芝加哥苏州衢州西宁西安贵港贵阳赤峰运城郑州重庆铁岭镇江长沙长沙市青岛香港香港特别行政区齐齐哈尔

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(2381) PDF downloads(200) Cited by(24)
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return