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Outline
- 1. Why Event-Driven Multi-GNSS ?
- 1. Sample High-Lat & Equatorial Results
High-latitude & Equatorial Ionospheric Scintillation Based on An - - PowerPoint PPT Presentation
High-latitude & Equatorial Ionospheric Scintillation Based on An - - PowerPoint PPT Presentation
High-latitude & Equatorial Ionospheric Scintillation Based on An Event-Driven Multi-GNSS Data Collection System Jade Morton, Yu Jiao, Steve Taylor Electrical and Computer Engineering Department Colorado State University 2015 IES Slide 1
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Amplitude Fading: Receiver Processing Artifacts
50 52 54 56 58 60 5 10 15 20 25 30 35 40 45
Time(sec) C/N0(dB-Hz)
Tracked C/N0 Simulated C/N0
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GPS Carrier Phase During Deep Fading: An Example
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0.5
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Time (ms)
Signal Intensity (dB) Carrier Phase (Cycles) 20 40 60 80
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- 40
0.5 0.5 1 1.5 CTL 10ms FPF 10ms FPF 40ms
*
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Issues: Conventional ISM Receivers High quality, raw GNSS signals are needed for space weather studies and robust GNSS receiver development
- 1. Accuracy
(Iono + other) X h(t) = Observed Effects Iono effects ≠ Observed Effects
- 2. Availability
Receivers cease to function during strong space weather events Data are not available when needed most!
- 3. Repeatability
Receiver processing is irreversible Ionosphere effects are wiped out during processing
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Event Driven Raw Data Collection System
Commercial ISM Receiver RF Front End 1 RF Front End 2 RF Front End N Space Weather Events Data Collection and Control Server Space Weather Event Monitoring & Trigger Software Circular Buffer Circular Buffer Circular Buffer Data Storage
VPN Data Center at Home Institution Internet
Specially designed signal tracking algorithms Scientific analysis Algorithm development
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Event-Driven Multi-Constellation GNSS Network
Ethiopia
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Equatorial Scintillation Spatial Distribution
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Diurnal Patterns
6 12 18 24 0.1 0.2 0.3 0.4 0.5 Hours after sunset
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Solar Cycle Dependence: High vs. Low Lat
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100
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800
Percent B Field Variation (nT) H D Z Percent of SV Affected
Geomagnetic Disturbance Impact on High Latitude
2 4 6 8 10 12 14 16 18 20 22 24
Time (Hours)
HAARP, AK 7/15/2012
500 1000 1500 2000 0.4 0.6 0.8 1 (nT) Probability of maxσφ >30o
maxH - minH maxD - minD maxZ - minZ (H2+D2+Z2)1/2 peak-to-peak
150 300 450 0.4 0.6 0.8 1 (nT)
σH σD σZ (σH 2+σD 2+σZ 2)1/2
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Frequency Diversity: Selective Fading
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Multi-Frequency Deep Fading
Carrier Phaser Reversal During Deep Fading
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Adaptive Joint Time-Frequency Analysis
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Irregularity Dynamics Sensing Using GNSS Array
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Array Processing: HAARP (Gakona, Alaska)
Lat: 62.39o, Lon: 145.15oW
North HF Heating Array Operation Center Ant 1 Ant 2 Ant 3 Science Pad 3 Ant 4 1km 3km ¼ km
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Commercial ISM Receiver SDR 1 GPS L1/GAL E1 SDR 3 GAL E5b/BDS B2 Data Collection and Control Server Space Weather Event Monitoring & Trigger Software VPN
Internet
SDR 6 GPS L2C SDR 7 BDS B1 SDR 2 GPS L5/GAL E5a SDR 4 GLO L1 SDR 5 GLO L2 Circular Buffer RAID Storage
OCXO
Gakona Poker Flat (65.1oN, 147.5oW) (62.3oN, 145.3oW) 9 % A u r
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a l
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a l b
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n d a r y
New Alaska Deployment
Poker Flat Advanced Modular Incoherent Scatter Radar (AMISR) Multi-Constellation GNSS Receiver Array
Ant 1 Ant 2 Ant 3
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Plasma Structure Dynamics Monitoring
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5 10 15 20 25 30 35 40 45 250 500 750 1000 1250 1500 1750
σφ (degrees) Available SuperDARN Data Points
Available SuperDARN Data Points vs. σφ Scintillation Low/no scintillation
Comparison with SuperDARN
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Novel GNSS Receiver Algorithms
- Adaptive Filtering
- Adaptive Inter-Channel Frequency Aiding
- Multi-Constellation Vector Processing
- Fixed Position Feedback
- Adaptive Drift Velocity Feedback
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Conclusions
- High quality GNSS data is needed for
– Continuous, accurate interpretation of ionosphere processes – Robust GNSS receivers development
- Successful data collection system yielding both known results
as well as new observations
– Adaptive processing is needed – Computation cost need to be improved
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Acknowledgements
- Funding support from:
– AFOSR, AFRL, NSF, DAGSI, Miami Univ., Colorado State Univ. – Industrial support: Rockwell Collins, Honeywell, Northrop Grumman, Mitre Co., Lockheed Martin, Topcon, Symmetricom, Septentrio, Novatel, John Deere.
- Collaborators:
– Ohio University, AFIT, University of Alaska Fairbanks, Singapore Nanyang Technical University, Hong Kong Polytechnic University, Boston College, Stanford University, University of Colorado Boulder, University of Hawaii – Arecibo Observatory, Jicamarca Radio Observatory, Poker Flat Rocket Range and HAARP, Sondrestrom Observatory.
- Students/Post-docs:
– Harrison Bourne, Steve Taylor, Jun Wang, Joy Jiao, Dongyang Xu, Brian Breitsch, Jack Hall, Brian Jamieson, Mark Carroll, Robert Cole, Hang Yin, Richard Marcus, Mellissa Simms, Fan Zhang, Kyle Wyan, Kyle Kauffman, Xiaolei Mao, Ruihui Di, Fei Niu, Ryan Wolfarth, Praveen Vikram, Dan Charney, Greg Distler, Greg Newstadt, Adam Hill, Matt Cosgrove, Nick Matteo, Aaron Pittenger, Priyanka Chandrasekaran, Cheng Wang, Xiaoli Liu, Senlin Peng, Nazalie Kassanbian, Lei Zhang, Xin Chen, Hu Wang, Hong Wu, Yanhong Kou.
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Common Volume LEO and Ground Observations
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Multi-Frequency Fading Analysis
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