A STATISTICAL MODEL OF IONOSPHERIC SIGNALS IN LOW-FREQUENCY SAR DATA
17
F.J Meyer 1) 2) , B. Watkins 3) 1) Earth & Planetary Remote Sensing, University of Alaska Fairbanks 2) Alaska Satellite Facility (ASF) 3) Space Physics and Aeronomy, University of Alaska Fairbanks A STATISTICAL MODEL OF IONOSPHERIC SIGNALS IN LOW-FREQUENCY SAR DATA Collaborating Organizations:
description
A STATISTICAL MODEL OF IONOSPHERIC SIGNALS IN LOW-FREQUENCY SAR DATA. F.J Meyer 1) 2) , B. Watkins 3) 1) Earth & Planetary Remote Sensing, University of Alaska Fairbanks 2) Alaska Satellite Facility (ASF) 3) Space Physics and Aeronomy, University of Alaska Fairbanks. - PowerPoint PPT Presentation
Transcript of A STATISTICAL MODEL OF IONOSPHERIC SIGNALS IN LOW-FREQUENCY SAR DATA
F.J Meyer1) 2), B. Watkins3) 1)Earth & Planetary Remote Sensing, University of Alaska Fairbanks
2)Alaska Satellite Facility (ASF)3)Space Physics and Aeronomy, University of Alaska Fairbanks
A STATISTICAL MODEL OF IONOSPHERIC SIGNALS IN LOW-FREQUENCY SAR DATA
Collaborating Organizations:
Ionospheric Artifacts in Radar DataWhat Do They Look Like?
2
Equ
ato
rial R
egio
ns
Pola
r Regio
ns
Phase Distortions
F. Meyer et al.IGARSS’11, Vancouver 3
Ionospheric Artifacts in Radar DataWhat Do They Look Like?
Rainforest, Brazil
[d
B]
Distance along profile [m]
-7
-9
-115000 10000 15000
Image Distortions
South-East Asia
F. Meyer et al.IGARSS’11, Vancouver 4
Ionospheric Distortions – How to Fix Them?
1. Signal Correction through Model Inversion:
203
00
02
0000
28.40428.40428.404ffTEC
fcffTEC
fcTEC
fc
Phase Distortions Image Distortions
Measure these!
TEC TEC TEC
Invert for these!
2. Statistical ModelingIf 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
F. Meyer et al.IGARSS’11, Vancouver 5
• Most small scale ionospheric turbulence 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 Ionospheric Turbulence
vkkP
F. Meyer et al.IGARSS’11, Vancouver 6
Power Law Model of Ionospheric Turbulence
• 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 2cos
Dv 27
→ Basis for signal analysis, statistical modeling, signal representation, and simulation
F. Meyer et al.IGARSS’11, Vancouver 7
• Representative power spectrum parameters are derived from global ionospheric scintillation model WBMOD (WideBand MODel)– WBMOD capable to simulate scintillation effects on user-defined system
based on solar activity and system parameters
– Prediction of power spectrum parameter for wide range of systems and ionospheric conditions
Power Spectra
Predicting Ionospheric Power Spectra
F. Meyer et al.IGARSS’11, Vancouver 8
• 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
8
F. Meyer et al.IGARSS’11, Vancouver 9
Relationship between Power Spectrum, Covariance Matrix, and Simulated Phase Screen
Spectral power: 2.9 rad2; Spectral Slope: 2.7
Covariance M
atrix
Spectral power: 2.9 rad2; Spectral Slope: 3.7
Covariance M
atrix
Pha
se S
imul
atio
n
Pha
se S
imul
atio
n
9
Location: Alaska
Date: 4/1/07
F. Meyer et al.IGARSS’11, Vancouver 10
Significance
• 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
F. Meyer et al.IGARSS’11, Vancouver 11
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 FunctionSample Covariance MatrixSample Phase Simulation
11
F. Meyer et al.IGARSS’11, Vancouver 12
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
12
Signal Variance ()
Covariance C(r)
Faraday Rotation from Full-Pol SAR
F. Meyer et al.IGARSS’11, Vancouver 13
Validation of IP-STATS in Polar Regions
• Validation of Signal Variance():
13
Model PredictionModel Prediction
Measured Signal VarianceMeasured Signal Variance
Case Study 1:Turbulent Ionosphere
Case Study 2:Quiet Ionosphere
At High Latitudes: Predicted and Measured Signal Variance Matches Well!!
F. Meyer et al.IGARSS’11, Vancouver 14
• Validation of Covariance FunctionC(r) :
• Measured Covariance: Isotropic signal assumed
• Simulated Covariance: Derived from sim
Validation of IP-STATS in Polar Regions
14
At High Latitudes: Predicted and Measured Covariance Functions match reasonably well!
Orbit: 6305; Frame: 1330 Orbit: 6305; Frame: 1390
F. Meyer et al.IGARSS’11, Vancouver 15
• Spaceborne imaging radars are affected by the ionosphere, in particular at times of high ionospheric turbulence
• Small scale ionospheric turbulence can be modeled statistically using power spectra, covariance functions, and fractal dimensions
• IP-STATS, a system for predicting statistical properties of ionospheric phase delay signals was presented
• First validations for Polar Regions show good performance of predicting variance and co-variance parameters
• Further validation is required, and anisotropy needs to be incorporated
Conclusions
15
F. Meyer et al.IGARSS’11, Vancouver 16
•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:
16
Open Three Year PhD Positionstarting 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
