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iono_statmodel_final-meyer.ppt

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  • 1. 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:
  • 2. Ionospheric Artifacts in Radar Data What Do They Look Like? Equatorial Regions Polar Regions Phase Distortions
  • 3. Ionospheric Artifacts in Radar Data What Do They Look Like? Rainforest, Brazil Image Distortions South-East Asia  [dB] Distance along profile [m] -7 -9 -11 5000 10000 15000
  • 4. Ionospheric Distortions – How to Fix Them?
    • Signal Correction through Model Inversion:
    • 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 Phase Distortions Image Distortions Measure these! Invert for these!
  • 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
  • 6. Power Law Model of Ionospheric Turbulence
    • On the convenience of power spectra:
      • Power Law models can be converted to covariance functions through cosine Fourier Transformation
      • Spectral slopes can be converted to fractal dimensions D
    -> Basis for signal analysis, statistical modeling, signal representation, and simulation
  • 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
    Predicting Ionospheric Power Spectra Power Spectra
  • 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
  • 9. Relationship between Power Spectrum, Covariance Matrix, and Simulated Phase Screen Spectral power: 2.9 rad 2 ; Spectral Slope: 2.7 Covariance Matrix Phase Simulation Location: Alaska Date: 4/1/07 Spectral power: 2.9 rad 2 ; Spectral Slope: 3.7 Covariance Matrix Phase Simulation
  • 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
  • 11.
    • 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 Geophysical Input Parameters Statistical Ionospheric Descriptors Sample Covariance Function Sample Covariance Matrix Sample Phase Simulation
  • 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
     Signal Variance  (  ) Covariance C (r) Faraday Rotation from Full-Pol SAR
  • 13. Validation of IP-STATS in Polar Regions
    • Validation of Signal Variance   (  ) :
    Model Prediction Measured Signal Variance At High Latitudes: Predicted and Measured Signal Variance Matches Well!! Case Study 1: Turbulent Ionosphere Case Study 2: Quiet Ionosphere
  • 14.
    • Validation of Covariance Function  C (r) :
      • Measured Covariance: Isotropic signal assumed
      • Simulated Covariance: Derived from  sim
    Validation of IP-STATS in Polar Regions At High Latitudes: Predicted and Measured Covariance Functions match reasonably well! Orbit: 6305; Frame: 1330 Orbit: 6305; Frame: 1390
  • 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 PALSAR Interferogram Amazon area, Ionospheric Disturbances
  • 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: PALSAR Interferogram Amazon area, Ionospheric Disturbances
  • 17. 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_address] ) and at: www.insar.alaska.edu