GNSS-based simulation of spacecraft formation flight: A case study of ionospheric plasma remote sensing

被引:4
|
作者
Peng, YuXiang [1 ,2 ]
Scales, Wayne [1 ]
机构
[1] Virginia Tech, Ctr Space Sci & Engn Res, Blacksburg, VA 24061 USA
[2] Qualcomm Technol Inc, Santa Clara, CA USA
来源
RADIATION EFFECTS AND DEFECTS IN SOLIDS | 2020年 / 175卷 / 11-12期
关键词
GNSS; GPS; spacecraft formation flight; ionospheric plasma; hardware-in-the-loop simulation; TIEGCM;
D O I
10.1080/10420150.2020.1845689
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
Future space weather missions using spacecraft formation flying can provide more robust, flexible, sustainable, and low-cost observational capability on multi-scale ionospheric plasma structures. The Virginia Tech Formation Flying Testbed (VTFFTB), a hardware-in-the-loop simulation testbed using multi-constellation, multi-frequency global navigation satellite systems (GNSS), has recently been developed to simulate closed-loop, real-time spacecraft formation flight with a group of 2 or 3 satellites at low Earth orbits (LEO). Onboard GNSS receivers are used for formation navigation as well as ionospheric plasma irregularities measurements. In a VTFFTB simulation, the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) was integrated to simulate Equatorial plasma bubbles (EPB) and study the EPB impacts on GNSS signals tracked by LEO formation satellites. This case study demonstrates the VTFFTB application to study the ionospheric plasma impacts on GNSS-related technologies using global space weather models and facilitates development of new ionospheric remote sensing techniques.
引用
收藏
页码:998 / 1001
页数:4
相关论文
共 50 条
  • [21] Exploring AI Progress in GNSS Remote Sensing: A Deep Learning Based Framework for Real-Time Detection of Earthquake and Tsunami Induced Ionospheric Perturbations
    Ravanelli, Michela
    Constantinou, Valentino
    Liu, Hamlin
    Bortnik, Jacob
    [J]. RADIO SCIENCE, 2024, 59 (09)
  • [22] AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada
    Parisouj, Peiman
    Khani, Hadi Mohammadzadeh
    Islam, Feroz
    Jun, Changhyun
    Bateni, Sayed M. M.
    Kim, Dongkyun
    [J]. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 2023, 59 (02): : 299 - 316
  • [23] Urban development trend analysis and spatial simulation based on time series remote sensing data: A case study of Jinan, China
    Zhang, Yanghua
    Zhao, Liang
    Zhao, Hu
    Gao, Xiaofeng
    [J]. PLOS ONE, 2021, 16 (10):
  • [24] Glacier Mass Loss Simulation Based on Remote Sensing Data: A Case Study of the Yala Glacier and the Qiyi Glacier in the Third Pole
    Yao, Ruzhen
    Shi, Jiancheng
    [J]. REMOTE SENSING, 2022, 14 (20)
  • [25] Remote Sensing, GIS Application and Simulation of Coastal Land Use Changes Based on Cellular Automata: A Case Study of Maputo, Mozambique
    Henriques, C.
    Tenedorio, J. A.
    [J]. JOURNAL OF COASTAL RESEARCH, 2009, : 1518 - 1521
  • [26] Case study of the 20 May 2008 tornadic storm in Hungary - Remote sensing features and NWP simulation
    Putsay, M.
    Simon, A.
    Szenyan, I.
    Kerkmann, J.
    Horvath, Gy.
    [J]. ATMOSPHERIC RESEARCH, 2011, 100 (04) : 657 - 679
  • [27] Earthquake damage assessment based on remote sensing data. The Haiti case study
    Ajmar, Andrea
    Boccardo, Piero
    Tonolo, Fabio Giulio
    [J]. ITALIAN JOURNAL OF REMOTE SENSING-RIVISTA ITALIANA DI TELERILEVAMENTO, 2011, 43 (02): : 123 - 128
  • [28] Estimation of regional evapotranspiration based on remote sensing: case study in the Heihe River Basin
    Yang, Yongmin
    Su, Hongbo
    Zhang, Renhua
    Tian, Jing
    Yang, Siquan
    [J]. JOURNAL OF APPLIED REMOTE SENSING, 2012, 6
  • [29] Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study
    Gu, Fengguan
    Zhang, Rui
    Tian-Kunze, Xiangshan
    Han, Bo
    Zhu, Lei
    Cui, Tingwei
    Yang, Qinghua
    [J]. REMOTE SENSING, 2021, 13 (05) : 1 - 16
  • [30] Evolution of spit morphology: a case study using a remote sensing and statistical based approach
    Avinash, Kumar
    Deepika, B.
    Jayappa, K. S.
    [J]. JOURNAL OF COASTAL CONSERVATION, 2013, 17 (03) : 327 - 337