Interferogram Filtering vs Interferogram Subtraction

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Presentation transcript:

Interferogram Filtering vs Interferogram Subtraction E. Rodriguez Jet Propulsion Laboratory California Institute of Technology

UHF Fringe Spectrum No Antenna Pattern Interferogram spectra for signal to clutter ratio of 1, radar frequency of 430MHz, bandwidth of 6MHz, for the first 50 km of xb. The basal spectrum is colored orange. The remaining curves show the surface spectra for D = 1 km (black), D = 2 km (red), D = 3 km (green), D = 4 km (blue). Notice that the basal fringe spectrum depends very weakly on depth

Interferogram Spectrum and Angular Variations of Brightness Complex interferogram Surface interferometric phase difference Basal interferometric phase difference The effect of long wavelength variations (due to the antenna pattern or sigma0) is to convolve the interferogram spectrum with the envelope spectrum. This can lead to significant spectral overlap.

Observed Surface Sigma0 Angular Dependence at 120 MHz Data obtained with the JPL Europa Testbed Sounder in deployment with the Kansas U. sounder over Greenland Angular decay near nadir (>15 dB in 5 degrees) consistent with very smooth ice surface Change in behavior at P-band is still unknown, but probably bounded by 1-3 degree slope models

Topographic Simulation Results Topography Weighted by Gain Surface Interferogram Basal Interferogram Distance (km) Range Range

Antenna Pattern Results: 2 km Surface Return Basal Return Before filtering After filtering

Antenna Pattern Results: 3 km What should be done about long wavelength brightness variations? Basal Return Surface Return Before filtering After filtering

Proposed Solution Rather than do blind Fourier filtering, treat surface signal as known up to a multiplicative constant (or similar low order polynomial), which is estimated by fitting and the interferogram phase known up to a constant + slope terms. The fitted signal is removed by subtraction. Known parameters: antenna gain, flat surface interferogram rate. Unknown parameters: surface slope, precise sigma0 variation. First sigma0 estimate from azimuth averaged intensity data. Validity of subtraction scheme has been shown to work on simulated data. Significantly better clutter reduction than Fourier filtering. Needs to be verified with real data