RECENT ADVANCES IN THE CORRECTION OF IONOSPHERIC EFFECTS...
Transcript of RECENT ADVANCES IN THE CORRECTION OF IONOSPHERIC EFFECTS...
F.J Meyer1) 2), B. Watkins3) , J.S. Kim4) , K. Papathanassiou4)
1)Earth & Planetary Remote Sensing, University of Alaska Fairbanks 2)Alaska Satellite Facility (ASF)
3)Space Physics and Aeronomy, University of Alaska Fairbanks 4)High Frequency and Radar Institute, German Aerospace Center (DLR), Germany
RECENT ADVANCES IN THE CORRECTION OF IONOSPHERIC EFFECTS IN LOW-FREQUENCY SAR
DATA
Collaborating Organizations:
F. Meyer et al. CEOS’11, Fairbanks, AK 2
Methods for Ionospheric Correction of SAR
• Ionosphere causes range of effects especially in low-frequency SAR systems
Ionosphere (TEC)
Faraday Rotation
Azimuth Shift
Range Shift
Interferometric phase (differential)
Dispersion
Ionospheric Artifacts in Radar Data
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Am
plitu
de D
isto
rtion
s
Phase D
istortions
Rainforest, Brazil
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Ionospheric Distortions – How to Fix Them?
1. Mathematical Modeling and Inversion:
( ) ( )2
0300
020000
28.40428.40428.404 ffTECfc
ffTECfc
TECfc
−⋅−−⋅+−=∆πππψ
Phase Distortions Image Distortions
Measure these!
TEC TEC TEC
Invert for these!
2. Statistical Modeling If mathematical modeling fails, statistical modeling mitigates effects on final target parameters Realistic modeling of the accuracy and correlation of data
→ Image correction & creation of ionospheric maps
TEC = Total Electron Content of Ionosphere
Mathematical Modeling and Inversion
Faraday Rotation (FR) Based Inversion (Freeman, 2004; Quegan, 2010; Nicoll & Meyer, 2008)
Range Split-Spectrum Based Inversion (Rosen, 2010; Papathanassiou, 2009; Meyer & Bamler, 2005; Brcic & Meyer, 2010)
Transmitted ground level Ω
Azimuth Autofocus Based Inversion (Papathanassiou, 2008; Meyer & Nicoll, 2008; Meyer, 2010)
Hybrid Methods (Meyer, 2005; Meyer, 2010; Meyer & Liu, 2011)
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Advances in Optimizing Mapping Performance
Method 1
Method 2
Method 3
Method 5
Method 6
Method 7
∫
∫
∫
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• Mitigation of ionospheric effects from Faraday Rotation and azimuth- shit estimates → reduced phase distortion and
improved coherence
Optimizing Ionospheric Correction C
oher
ence
com
paris
on:
Bef
ore
and
afte
r cor
rect
ion
Before correction
FR correction
Original phase Corrected phase
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Cou
rtesy
of J
un S
u K
im, D
LR
FR & Az. shifts
In Case Signal Correction Fails:
• Statistical Modeling: • Statistical modeling mitigates effects on final target parameters through
realistic modeling of the accuracy and correlation of data
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• Most small scale variations of ionospheric delay can be described as featureless, scale invariant noise like signals
• Convenient Descriptor: Power Law Functions
• Indicates:
– Total power of signal – Distribution of power over spatial scales
• Spectral Slope v: – Steep → smooth signal – Shallow → noisy signal
• Spectral slopes between ~2 and ~5 have been observed
Power Law Model of Small-Scale Ionospheric Signals
( ) vkkP −∝ϕ
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Power Law Model of Small-Scale Ionospheric Signals
• On the convenience of power spectra: 1. Power Law models can be converted to covariance functions through cosine
Fourier Transformation
2. Spectral slopes can be converted to fractal dimensions D
( ) ( ) ( )∫= dffPfrrC ϕϕ π2cos
Dv 27 −=
→ Basis for signal analysis, statistical modeling, signal representation, and simulation
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• Representative power spectrum parameters are derived from global ionospheric scintillation model WBMOD (WideBand MODel) – WBMOD capable to simulate statistical properties of scintillation effects on
user-defined system based on solar activity and system parameters
– Prediction of single-regime power spectrum parameters for wide range of systems and ionospheric conditions
Power Spectra
Predicting Ionospheric Power Spectra
E.J. Fremouw & J.A. Secan (1984): Modeling and Scientific Application of Scintillation Results, Radio Science, 19(3), pp 687 – 694.
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• Workflow of the Ionospheric Phase Statistics Simulator (IP-STATS)
IP-STATS: A System for Describing and Simulating the Ionosphere
Power Spectra
Covariance Function
Covariance Matrix
Signal Simulation
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Potential Significance of IP-STATS
• Only dependent on quantifiable ionospheric and system parameters and no requirement for real observations
• Covariance Functions and Matrices: – Can support realistic statistical models to be used in parameter estimation
• Phase Simulations: – Sensitivity analysis of spaceborne radar systems – Useful in System design analysis – Selection of best suited radar system for an application
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Geophysical Input Parameters
• Data point every 46 days
• Real PALSAR orbit and acquisition parameters
Simulating Ionospheric Conditions for a 10-Year Time Series of SAR Acquisitions over the North Slope of Alaska
Test Site
Statistical Ionospheric Descriptors
Sample Covariance Function Sample Covariance Matrix Sample Phase Simulation
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Validation of IP-STATS in Polar Regions
• Real data processing:
• IP-STATS: • Ionospheric phase statistics parameter from SAR system parameters,
observation geometry, and solar parameters at acquisition time
• COMPARISON: • Validation for Auroral Zone conditions
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Σ Signal Variance σ(φ)
Covariance C(r)
Faraday Rotation from Quad-Pol SAR
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Validation of IP-STATS in Polar Regions
• Validation of Signal Variance σ(φ):
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At High Latitudes: Predicted and Measured Signal Variance Matches Well!!
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• Validation of Covariance Function C(r) : • Measured Covariance: Isotropic signal assumed
• Simulated Covariance: Derived from ϕsim
Validation of IP-STATS in Polar Regions
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At High Latitudes: Predicted and Measured Covariance Functions match reasonably well!
Orbit: 6305; Frame: 1330 Orbit: 6305; Frame: 1390
Problem: Isotropic signal assumed Most observed ionospheric signals are NOT isotropic
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Anisotropy Model in IP-STATS
• Current approach – extract information from WBMOD • Anisotropy approximated by “Correlation Ellipse”:
– Shape: axial ratios a and b (length of axes of correlation ellipse relative to vertical layer thickness thin layer approximation is used)
– Orientation: angle δ relative to local ionospheric L-shell
L-Shells
5 4 3
2
1.5
• a = 10; b = 1; δ = 0 Rod-like oriented roughly east-west
• a = 10; b = 10; δ = 0 Sheet-like
• Examples:
D.G. Singleton (1970): Dependence of Satellite Scintillations on Zenith Angle and Azimuth, Journal of Atmospheric and Terrestrial Physics, 32, pp 789 – 803.
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Anisotropy Model in IP-STATS
• a = 3; b = 1; δ = 0 Rod-like oriented roughly east-west
• April 1, 2007:
WBMOD Estimate
Observed SAR Phase distortions on April 1, 2007
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• We have shown recent work:
– Advanced (combined) analytical correction methods show promise
– IP-STATS system models statistical properties of small scale ionospheric irregularities based on power spectra, covariance functions, and fractal dimensions
– First validations for Polar Regions show good performance of predicting variance and co-variance parameters
• Next steps: – Combination of more than two detection methods
– Further validation and incorporation of anisotropy in statistical model – Investigation of multi-scale power spectra for statistical model
Conclusions
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Open Three Year PhD Position starting fall 2011 / spring 2012 for a radar remote sensing research project at the Geophysical
Institute of the University of Alaska Fairbanks on
Theoretical Investigations into the Impact and Mitigation of Ionospheric Effects on Low-Frequency SAR and InSAR Data
Research Focus:
• Investigation of spatial and temporal properties of ionospheric effects in SAR data
• Development of statistical signal models
• Design of optimized methods for ionospheric correction
More information: Dr. Franz Meyer ([email protected]) and at: www.insar.alaska.edu
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• Funding was provided by: – NASA EPSCoR Research Initiation Grant “Structural and Statistical
Properties of Ionospheric Effects in Space‐based L‐Band SAR Data” – NASA ROSES 2009 “Remote Sensing Theory” Grant “Collaborative
Research: Theoretical Investigations into the Impact and Mitigation of Ionospheric Effects on Low-Frequency SAR and InSAR”
• The ionospheric model WBMOD was provided by Northwest Research
Associates (NWRA)
• ALOS PALSAR data were provided by the Alaska Satellite Facility (ASF) and the Japanese Aerospace Exploration Agency (JAXA)
Acknowledgments:
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