Please wait a minute...
img

官方微信

遥感技术与应用  2021, Vol. 36 Issue (3): 594-604    DOI: 10.11873/j.issn.1004-0323.2021.3.0594
数据与图像处理     
星载差分吸收气压雷达的系统仿真与性能分析
胥鑫1,2(),朱迪1()
1.中国科学院国家空间科学中心,北京 101400
2.中国科学院大学,北京 100049
Simulation and System Design of Spaceborne Differential Absorption Barometric Radar
Xin Xu1,2(),Di Zhu1()
1.National Space Science Center,Chinese Academy of Sciences,Beijing 101400,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
 全文: PDF(3186 KB)   HTML
摘要:

提出一种工作在65~70 GHz强氧气吸收波段的星载差分吸收气压雷达,用以连续获取全球高时空分辨率的海面气压数据。通过对星载差分吸收气压雷达系统设计需求的分析,利用大气廓线数据和大气吸收系数模型,对海面气压差分吸收的性能进行了仿真及性能分析。仿真结果表明:通过强氧气吸收波段下的海面气压和差分吸收指数之间存在线性关系,星载差分吸收气压雷达在66 GHz与69 GHz的工作频率下晴空时得到的海面气压估计的均方根误差在2.6 mbar,不同云况下得到的海面气压估计的均方根误差在3~4 mbar,为后续雷达系统的设计与工程实现提供了参考与依据。

关键词: 差分吸收气压雷达海面气压测量雷达系统仿真主动遥感    
Abstract:

A spaceborne differential absorption barometric pressure radar operating in the 65~70 GHz strong oxygen absorption band is proposed to continuously acquire sea surface pressure data with global high temporal and spatial resolution. Through the analysis of the design requirements of the spaceborne differential absorption barometric radar system, the atmospheric profile data and the atmospheric absorption coefficient model are used to simulate and analyze the performance of the sea surface pressure differential absorption. The simulation results show that there is a linear relationship between the sea surface pressure in the strong oxygen absorption band and the differential absorption index. The RMSE of the sea surface pressure estimation obtained by the spaceborne differential absorption barometric pressure radar under clear sky at operating frequencies of 66 GHz and 69 GHz. The error is 2.6 mbar, and the rms error of the sea surface pressure estimation obtained under different cloud conditions is 3 to 4 mbar, which provides a reference and basis for the design and engineering implementation of the subsequent radar system.

Key words: Differential absorption technology    Sea surface pressure measurement    Radar system simulation    Active remote sensing
收稿日期: 2020-04-11 出版日期: 2021-07-22
ZTFLH:  TP75  
基金资助: 微小卫星大气微波探测有效载荷及应用技术(D040301)
通讯作者: 朱迪     E-mail: xuxint2@163.com;zhudi@mirslab.cn
作者简介: 胥鑫(1995-),男,陕西咸阳人,硕士研究生,主要从事遥感探测等方面的研究。E-mail: xuxint2@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
胥鑫
朱迪

引用本文:

胥鑫,朱迪. 星载差分吸收气压雷达的系统仿真与性能分析[J]. 遥感技术与应用, 2021, 36(3): 594-604.

Xin Xu,Di Zhu. Simulation and System Design of Spaceborne Differential Absorption Barometric Radar. Remote Sensing Technology and Application, 2021, 36(3): 594-604.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.3.0594        http://www.rsta.ac.cn/CN/Y2021/V36/I3/594

图1  50~70 GHz美国标准大气天底光学厚度随频率的变化
图2  50~70 GHz不同风速下的海面后向散射系数
图3  仿真采用的60轨大气廓线数据轨迹
图4  星载差分吸收气压雷达观测几何
图5  脉冲时序的PRF约束
图6  星载差分吸收气压雷达的脉冲时序
参数
轨道高度500 km
工作频率65~70 GHz
有效天线口径1 m
天线增益>55 dB
水平分辨率3 km(nadir)
垂直分辨率300 m(nadir)
信号带宽500 k
脉冲宽度995 μs
脉冲重复频率428 HZ
发射功率100 W
噪声系数6 dB
系统参考温度290 K
表1  星载差分吸收气压雷达仿真系统参数
图7  星载差分吸收气压雷达仿真流程
图8  69 GHz的频率组合的均方根误差
图9  65~70 GHz美国标准大气双程大气衰减随频率的变化
图10  67~69 GHz、66~69 GHz、65~69 GHz频率组合下差分吸收指数与海面气压之间的仿真关系
氧气的吸收谱线
65~69 GHz65.224 165.767 466.302 0
66~69 GHz66.836 767.369 467.900 7
67~69 GHz68.430 868.960 169.488 7
表2  65~70 GHz内氧气的吸收谱线(GHz)[26]
图11  不同温度廓线下海面气压与差分吸收指数之间的关系(a) 4种晴空温度廓线 (b) 66~69 GHz频率组合
图12  不同温度廓线下海面气压与差分吸收指数之间的关系(a) 4种晴空湿度廓线 (b) 66~69 GHz频率组合
图13  无云晴天(Clear-Sky)、卷云(Cirrus)、高层云(Altostratus)、高积云(Altocumulus)、层云(Stratocumulus)、积云(Cumulus)条件下,66~69 GHz频率组合的差分吸收指数与海面气压之间的关系
1 Lin B, Hu Y. Numerical Simulations of Radar Surface Air Pressure Measurements at O2 Bands[J]. IEEE Geoscience and Remote Sensing Letters, 2005, 2(3):324-328.
2 Zhang Z, Dong X, Liu L, et al. Retrieval of Barometric Pressure from Satellite Passive Microwave Observations over the Oceans[J]. Journal of Geophysical Research, 2018, 123(6): 4360-4372.
3 Flower D, Peckham G. A Microwave Pressure Sounder[R]. Pasadena, CA: Jet Propulsion Laboratory, 1978.
4 Lin B, Hu Y. Numerical Simulations of Radar Surface Air Pressure Measurements at O/Sub 2/Bands[J]. IEEE Geoscience and Remote Sensing Letters, 2005, 2(3): 324-328.
5 National Research Council. Handbook of Frequency Allocations and Spectrum Protection for Scientific Uses[M]. Washington,D.C, National Academies Press, 2007.
6 Ulaby F T, Moore R K, Fung A K. Microwave Remote Sensing: Active and Passive. Volume 1-Microwave Remote Sensing Fundamentals and Radiometry[M]. Dedham,Massachusetts: Artech House, 1986: 721-820..
7 Swinehart, D. F. The Beer-Lambert Law[J]. Journal of Chemical Education, 1962, 39(7): 333.
8 Millán L, Lebsock M, Livesey N, et al. Differential Absorption Radar Techniques:Surface Pressure[J]. Atmospheric Measurement Techniques, 2014, 7(11): 3959-3970.
9 Lin B, Hu Y, Harrah S, et al. The Feasibility of Radar-based Remote Sensing of Barometric Pressure[J]. Final Report,NASA Earth Science Technology Office(ESTO),2006,2(3):324-328.
10 Ku L. Research on the Correction Method of Narrow-band RCS Measurement of Atmospheric Absorption Attenuation[D]. Xi'an:Xidian University,2012.
10 库流杰.窄带RCS测量大气吸收衰减修正方法研究[D].西安:西安电子科技大学,2012.
11 Liebe H J. MPM—An Atmospheric Millimeter-wave Propagation Model[J]. International Journal of Infrared and Millimeter Waves, 1989, 10(6): 631-650.
12 Boisot O, Nouguier F, Chapron B, et al. The GO4 Model in Near-nadir Microwave Scattering from the Sea Surface[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(11): 5889-5900.
13 NouGuier,Frédéric, et al. Analysis of Dual-frequency Ocean Backscatter Measurements at Ku-and Ka-bands Using Near-nadir Incidence GPM Radar Data.[J]. IEEE Geoscience and Remote Sensing Letters2016,13(9): 1310-1314.
14 Partain P. Cloudsat ECMWF-AUX Auxiliary Data Process Description and Interface Control Document[J]. Cooperative Institute for Research in the Atmosphere, Colorado State University, 2007,2394:1466-1477.
15 Li F K, Im E, Durden S L. Cloud Profiling Radar (CPR) for the CloudSat Mission[C]∥ Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International. IEEE, 2000.
16 Chan M A, Comiso J C. Arctic Cloud Characteristics as Derived from MODIS, CALIPSO, and CloudSat[J]. Journal of Climate,2013, 26(10):3285-3306.
17 Patoux J, Foster R C, Brown R A. An Evaluation of Scatterometer-derived Oceanic Surface Pressure Fields[J].Journal of Applied Meteorology and Climatology,2008,47(3):835-852.
18 Karapantazis S, Pavlidou F N. Design Issues and QoS Handover Management for Broadband LEO Satellite Systems[J]. IEE Proceedings-Communications, 2005, 152(6): 1006-1014.
19 Cho D H, Kim H D. Analysis of Orbit Determination of the KARISMA Using Radar Tracking Data of a LEO Satellite[J]. Journal of the Korean Society for Aeronautical & Space Sciences, 2015, 43(11): 1016-1027.
20 Muri P, Challa O, McNair J. Enhancing Small Satellite Communication Through Effective Antenna System Design[C]∥ Military Communications Conference. IEEE, 2010.
21 Stephens G, Winker D, Pelon J, et al. CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System[J]. Bulletin of the American Meteorological Society, 2018, 99(3): 569-581.
22 Lonfat M. Precipitation Distribution in Tropical Cyclones Using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager: A Global Perspective[J]. Health Research Policy and Systems,2004, 11(1):40-40.
23 National Research Council. Handbook of Frequency Allocations and Spectrum Protection for Scientific Uses[M]. National Academies Press, 2007.
24 Wang Gang, Dong Xiaolong, Zhu Di. High-resolution Implementation based on Spaceborne Rotating Scanning Radar[J]. Remote Sensing Technology and Application,2017,32(6):1071-1077.
24 王刚,董晓龙,朱迪.基于星载旋转扫描雷达的高分辨率实现[J]. 遥感技术与应用,2017,32(6):1071-1077.
25 Zhang Zijin, Dong Xiaolong. Application of Passive Microwave Remote Sensing Sea Surface Pressure in the Analysis of Tropical Cyclones[C]∥ The 35th Annual Meeting of the Chinese Meteorological Society, 2018.[张子瑾, 董晓龙. 被动微波遥感海面气压在热带气旋分析中的应用[C]∥ 第35届中国气象学会年会, 2018.]
26 Liebe H J, Hufford G A, Cotton M G. Propagation modeling of Moist Air and Suspended Water/Ice Particles at Frequencies below 1000 GHz[C]∥ AGARD 52nd Specialists' Meeting of the Electromagnetic Wave Propagation Panel, 1993, 52(11): 2058-2072.
27 Rosenkranz P W.Water Vapor Microwave Continuum Absorption: A Comparison of Measurements and Models[J]. Radio Science, 1998,33(4): 919-928.
28 Vesecky J F, Onstott R G, Wang N Y, et al. Water Surface Temperature Estimates Using Active and Passive Microwave Remote Sensing: Preliminary Results from an Outdoor Wind-wave Tank[C]∥ Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation. International. IEEE, 1994.
29 Che L,Ding Y J,Peng Z M. The Influences of Temperature, Concentration and Pressure Uncertainties on the Measurement Results of Wavelength Modulation Spec-troscopy[J]. Chinese Physics Letters, 2012, 29(6):67801-67804.
30 Kollias P, Albrecht B A, Marks Jr F. Why Mie? Accurate Observations of Vertical Air Velocities and Raindrops Using a Cloud Radar[J]. Bulletin of the American Meteorological Society, 2002, 83(10): 1471-1484.
[1] 李宝芸,范玉刚,杨明莉. 基于LFDA和GA-ELM的高光谱图像地物识别方法研究[J]. 遥感技术与应用, 2021, 36(3): 587-593.
[2] 盛夏,石玉立,丁海勇. 青藏高原GPM降水数据空间降尺度研究[J]. 遥感技术与应用, 2021, 36(3): 571-580.
[3] 邵文静,孙伟伟,杨刚. 高光谱遥感影像纹理特征提取的对比分析[J]. 遥感技术与应用, 2021, 36(2): 431-440.
[4] 祝一诺,高婷,王术东,周磊,杜明义. 基于迁移学习再训练模型和高分遥感数据的建筑垃圾自动识别方法[J]. 遥感技术与应用, 2021, 36(2): 314-323.
[5] 林娜,冯丽蓉,张小青. 基于优化Faster-RCNN的遥感影像飞机检测[J]. 遥感技术与应用, 2021, 36(2): 275-284.
[6] 徐光志,徐涵秋. Sentinel⁃2A MSI 和Landsat 8 OLI两种传感器多光谱信息的交互对比[J]. 遥感技术与应用, 2021, 36(1): 165-175.
[7] 康翔,潘剑君,朱燕香,白浩然,卢晓丽. 一种基于POI大数据的城市核心区识别方法[J]. 遥感技术与应用, 2021, 36(1): 237-246.
[8] 周怡 马佳义 黄珺. 基于互导滤波和显著性映射的红外可见光图像融合[J]. 遥感技术与应用, 0, (): 1275-0.
[9] 周怡,马佳义,黄珺. 基于互导滤波和显著性映射的红外可见光图像融合[J]. 遥感技术与应用, 2020, 35(6): 1404-1413.
[10] 徐艳豪,刘晓龙,史正涛,高书鹏. 热带地区Landsat干季数据的无效像元修复方法研究[J]. 遥感技术与应用, 2020, 35(6): 1394-1403.
[11] 柴栋,许夙晖,罗畅,鲁彦辰. 使用贝叶斯优化对遥感影像目标进行精确定位[J]. 遥感技术与应用, 2020, 35(6): 1377-1385.
[12] 徐瑾昊,冯敏,王建邦,冉有华,祁元,杨联安,李新. 基于高分遥感数据和深度学习的石冰川自动提取研究[J]. 遥感技术与应用, 2020, 35(6): 1329-1336.
[13] 雷华锦,李弘毅,王建,郝晓华,赵宏宇,张娟. 基于环境信息和回归模型的青藏高原MODIS积雪面积比例产品制备[J]. 遥感技术与应用, 2020, 35(6): 1303-1311.
[14] 岳珊娜,车涛,戴礼云,肖林,邓婕. 北半球及典型区雪深时空分布与变化特征[J]. 遥感技术与应用, 2020, 35(6): 1263-1272.
[15] 王明,刘正佳,陈元琰. 基于Sentinel-2波段/产品的图像云检测效果对比研究[J]. 遥感技术与应用, 2020, 35(5): 1167-1177.